Dual energy microCT for small animal imaging Carmen …...werden enkele materiaalseparatiealgoritmes...

Carmen Bouckaert

Dual energy microCT for small animal imaging

Academiejaar 2011-2012Faculteit Ingenieurswetenschappen en ArchitectuurVoorzitter: prof. dr. ir. Jan Van CampenhoutVakgroep Elektronica en Informatiesystemen

Master of Biomedical EngineeringMasterproef ingediend tot het behalen van de academische graad van

Begeleider: Bert VandeghinstePromotoren: prof. Christian Vanhove, prof. dr. Stefaan Vandenberghe

De auteur geeft de toelating deze scriptie voor consultatie beschikbaar te stellen en delenvan de masterproef te kopieren voor persoonlijk gebruik.Elk ander gebruik valt onder de beperkingen van het auteursrecht, in het bijzonder metbetrekking tot de verplichting de bron uitdrukkelijk te vermelden bij het aanhalen vanresultaten uit deze masterproef.

The author gives permission to make this master dissertation available for consultationand to copy parts of this master dissertation for personal use.In case of any other use, the limitations of the copyright have to be respected, in particu-lar with regard to the obligation to state expressly the source when quoting results fromthis master dissertation.

Carmen Bouckaert, 4 juni 2012

Preface

This thesis would not have been possible without the help of many people.

First of all, I would like to thank my supervisor Bert Vandeghinste for his help andsupport during the course of my thesis. He has given me the trust and opportunity toaddress the subject of my thesis in a way I found the most appropriate. I found thisvery challenging, but at the same time also very enriching. Furthermore, I would liketo show my gratitude to my promoters, prof. Christian Vanhove and prof. dr. StefaanVandenberghe, for giving me the chance to do my thesis in their department and creatingan additional, more experimental subject for me.

In addition, I would also like to thank the personnel of the INFINITY lab. In parti-cular, I would like to thank Scharon Bruneel for her help during the in vivo animal tests,as well as Benedicte Descamps for informing about the availability of the the scanner andthe reconstruction computer.

I would also like to thank my fellow student Tom D.S. for the many discussions wehad on our thesis subjects which brought me to new insights and ideas and prevented mefrom making stupid errors.

Furthermore, I would like to thank my parents and grandparents for their support duringthe last five years. Without their help I would not have been able to do this. A specialthanks to my youngest brother Brecht for his moral support, which is, as he stated it,”the hardest and most time consuming part of writing a thesis.”

Moreover, I would like to thank Eugene and everyone who feels addressed by this namefor their support during the last year and the fun we had at our thesis weekend.

Finally, a special thanks to my dad, Tom V.D.G. and Vincent for reviewing my the-sis and all of my friends who have supported me during these past five years.

Carmen BouckaertJune 2012

v

Dual energy microCT for small

animal imagingby

Carmen Bouckaert

Master thesis submitted to obtain the academic degree of

Master of Biomedical Engineering

Academic year 2011–2012

Promoters: Prof. Christian Vanhove, Prof. Dr. Stefaan Vandenberghe

Supervisor: Bert Vandeghinste

Faculty of Engineering

Ghent University

Department Elektronica en Informatiesystemen

Chairman: Prof. Dr. Ir. Jan Van Campenhout

Summary

In this thesis, the feasibility of dual energy microCT (DECT) on a standard FLEXTriumph-II microCT system was investigated. This technique offers a solution for thelack of material differentiating capabilities to which microCT is subjected. This may beimportant for cardiovascular studies where it is often necessary to separate iodine filledvascular structures from other tissues like bone. First, the optimal scan protocol forDECT was determined using phantoms. Next, material separation algorithms were ap-plied and evaluated on phantoms and in vivo and ex vivo data. Finally, a few applicationsof pre-clinical DECT were tested.

Samenvatting

In dit werk werd er nagegaan of duale energie microCT mogelijk is, gebruik makend vaneen standaard FLEX Triumph-II micro-CT systeem. Deze techniek biedt een oplossingvoor het gebrek aan materiaal differentiatie verbonden met microCT. Dit kan belangrijkzijn voor cardiovasculaire studies waarbij het vaak nodig is om met jood gevulde vasculairestructuren te scheiden van andere weefsels zoals bijvoorbeeld bot. Eerst werd er gezochtnaar een optimaal scan protocol voor DECT, gebruik makend van fantomen. Vervolgenswerden enkele materiaalseparatiealgoritmes toegepast en geevalueerd op zowel fantomenals in vivo en ex vivo data. Tenslotte werden ook nog enkele toepassingen van pre-klinischeDECT getest.

Keywords

DECT, microCT, in vivo, mice, material separation

Dual Energy microCT for small animal bone-iodinedecomposition

Carmen Bouckaert

Supervisor(s): Christian Vanhove, Stefaan Vandenberghe, Bert Vandeghinste

Abstract— In this work the feasibility of bone-iodine decomposition usingdual energy CT is tested on a standard pre-clinical FLEX Triumph-II system.Promising results were obtained using very simple decomposition techniques ona phantom and in vivo mouse data.

Keywords—microCT, DECT, in vivo, mice, material separation

I. INTRODUCTION

Dual Energy Computed Tomography (DECT) is becomingwell-established in clinical practice. By acquiring images at a lowand high energy, it allows to perform easy material separation orquantification based on the energy dependence of the attenuationcoefficient [1][2]. Various applications of DECT already exist inclinical practice e.g. dual energy CT angiography (DE-CTA) [3],quantification of urinary stones [4], detection of gout [5], or vir-tual non-contrast [6].Despite its numerous applications in clinical practice, the trans-lation of this technique to pre-clinical systems has not been in-vestigated thoroughly. The aim of this work is therefore to vali-date if in vivo and ex vivo separation of bone and iodine is fea-sible using a standard FLEX Triumph-II system (Gamma-MedicaIdeas, Northridge, CA, USA), as microCT images are inherentlyprone to a higher amount of noise.Applications of pre-clinical DECT may be found in cardio-vascular studies were it is important to separate the different vas-cular structures from the surrounding non-vascular structures likebone.

II. MATERIALS AND METHODS

A. CT Acquisitions

The FLEX Triumph-II system was used to acquire the microCTimages. This system contains a 2mm aluminium filtered tubewith adjustable focal spot size. The CT images were obtainedusing 1024 projections, detected by a 2368 x 2240 detector withpixel size of 0.05mm. The acquisition time for each scan is ap-proximately 20 minutes. The microCT data were reconstructed toa 5123 voxel space with a voxel size of 0.04mm for the phantomand a voxel size of 0.1mm for the mice.

B. Optimal Energy Pair

Before applying a material decomposition algorithm, the opti-mal energy combination was determined. A density calibrationphantom (GE medical systems, London, Ontario, Canada) wasscanned together with different concentrations of iodine at peakvoltages ranging from 40 kVp to 90 kVp. The iodine concen-trations ranged from 25mgI/ml to 55mgI/ml and also somepure contrast agent with a concentration of 320mgI/ml was used.Nine energy pairs were considered, and no additional filtrationwas added to obtain a better separation of the energy spectra. Onthe reconstructed images a region of interest (ROI) was drawnover the different materials, and mean values and standard devia-tions were calculated in these regions. Next, the relationship be-tween the mean values from a low energy and a high-energy imagewas determined and plotted. Fig. 1 plots a linear fit through thedifferent iodine concentrations; the same holds for bone and softtissue.

Fig. 1. 50 -90 kVp energy couple. The green line connects the different iodineconcentrations, while the red line connects soft tissue with bone. The green andred shaded areas represent the 95% confidence interval.

C. Decomposition Algorithm

A material separation algorithm can be applied after image re-construction. This three material decomposition algorithm as-sumes the volume fractions of each basis material in a voxelshould add to unity (Eq. 1) [7]. As basis materials bone, iodineand soft tissue were chosen.

f1 + f2 + f3 = 1 (1)

Furthermore, the linear attenuation coefficient of a voxel in a CT-scan can be expressed as a linear combination of the three ba-sis materials multiplied by their respective volume fraction. ForDECT this leads to eqs. 2 and 3, where E1 and E2 are the meanenergies of the used spectra.

µ1,E1 · f1 + µ2,E1 · f2 + µ3,E1 · f3 = µE1 (2)

µ1,E2 · f1 + µ2,E2 · f2 + µ3,E2 · f3 = µE2 (3)

These three equations yield three unknowns f1, f2 and f3 as theattenuation coefficients for the different basis materials can befound using the mean values obtained with the density calibra-tion phantom.The values for f1, f2 and f3 can be calculated by solving eqs. 1, 2and 3 using a non-negative least squares method. As an additionalconstraint, all voxels with a HU below soft tissue are assumed tocontain no basis material and are thus not taken into account forthe calculations.To ensure correct operation of the algorithm, the animal bed con-taining copper guides was omitted from the images.

D. In vivo and ex vivo studies

To validate the feasibility of preclinical DECT material decom-position, some in vivo and ex vivo studies with mice were per-formed. The Ghent University ethical committee approved all ani-mal experiments (ECD 12/24). Five animals were anaesthetisedwith isoflurane, injected with 0.010ml/g Fenestra VC-131 R©

(Advanced Research Technologies Inc., Saint Laurent, Canada)and scanned in vivo after waiting 15 minutes. The animals werefirst scanned at 90 kVp, followed by a scan at 50 kVp. After thesescans were acquired, the animals were euthanised using an over-dose of pentobarbital. The animals were then scanned ex vivousing the same acquisition parameters.

Fig. 2. Validation of the material decomposition algorithm on the phantom. a.Original image b. Bone image (f1) c. Iodine image (f2) d. Soft tissue image(f3)

Fig. 3. Result of the material decomposition applied to the in vivo scans. a. Originalimage b. Bone image (f1) c. Iodine image (f2) d. Soft tissue image (f3)

III. RESULTS

A. Optimal Energy Pair

Fig. 1 shows one of the nine energy pairs that were considered.The optimal pair was selected based on the amount of separationbetween both curves and the precision of the fit to the measureddata. Based on visual analysis of these criteria, it was found thatthe 50 - 90 kVp energy pair yields the best results and therefore itwas decided to use this pair for further measurements. The expo-sure time per projection was adjusted to obtain equal contrast tonoise ratios in both the high and low energy images.

B. Phantom

Based on the previous results, the material separation algorithmwas first tested on the density calibration phantom. The results areshown in fig. 2. All iodine concentrations were correctly detectedas iodine. Bone was separated, although the edge of the iodinevials (glass) were also detected as being bone.

C. In vivo and ex vivo tests

The results of the material decomposition on the in vivo and exvivo images can be seen in fig. 3 and fig. 4, respectively. Wefocused on a slice through the heart, where it is most clear wherethe iodine should be located. The same slices were investigated inthe in vivo as well as the ex vivo scans. The iodine selective imagedistinguishes the cardiac ventricles. Some bone is still containedin the iodine selective image; similarly, the bone selective imagealso shows iodine contributions.

IV. DISCUSSION

Although the results show that material decomposition for boneand iodine is feasible with this pre-clinical system, more advancedtechniques will be necessary for in vivo data.

Fig. 4. Result of the material decomposition applied to the ex vivo scans. a. Originalimage b. Bone image (f1) c. Iodine image (f2) d. Soft tissue image (f3)

Fig. 2 shows that material decomposition in the phantom is quiteaccurate, apart from a mismatch between bone and glass. How-ever, this is not considered problematic, as under normal circum-stances no glass will be present in the animal’s body. The materialdecomposition applied to the in vivo images does not perform aswell as on the phantom images (Fig. 3). Nevertheless, we canstill distinguish both cardiac ventricles containing iodine. Further-more, it can be seen that the separation between bone and iodineis not perfect.Even though similar or better results are expected for the ex vivoscans, the material decomposition seems to be worse. A possibleexplanation is the relaxation of the muscles followed by stiffeningafter the euthanasia. This may result in movement of the organsand bones in the low energy scan compared to the high energyscan and may hamper correct material decomposition.

V. CONCLUSION

We have shown that bone-iodine decomposition is feasibleusing image data obtained with a standard FLEX Triumph-II sys-tem. As the decomposition is not yet perfect using these verysimple techniques, further research into more complex, raw databased algorithms may be required. Furthermore, the use of ad-ditional filtration to achieve better separation between the spectrashould be considered.

REFERENCES

[1] T.R.C. Johnson, B. Krauss, M. Sedlmair and M. Grasruck, ”Material differentiation by dualenergy CT: Initial experience,” European Radiology, vol. 17, pp. 1510-1517, 2007.

[2] M. Petersilka, H. Bruder, B. Krauss, and K. Stierstorfer, ”Technical principles of dual sourceCT,” European Journal of Radiology, vol. 68, pp. 362-368, 2008.

[3] Y. Watanabe, K. Uotani, T. Nakazawa, M. Higashi, N. Yamada, Y. Hori, S. Kanzaki, T.Fukuda, T. Itoh, and H. Naito, ”Dual-energy direct bone removal CT angiography for evalu-ation of intracranial aneurysm or stenosis: Comparison with conventional digital subtractionangiography,” European Radiology, vol. 19, pp. 1019-1024, 2009.

[4] N. Takahashi, R. P. Hartman, T. J. Vrtiska, A. Kawashima, A. N. Primak, O. P. Dzyubak, J.N. Mandrekar, J. G. Fletcher, and C. H. McCollough, ”Dual-energy CT iodine-subtractionvirtual unenhanced technique to detect urinary stones in an iodine-filled collecting system:A phantom study,” American Journal of Roentgenoloy, vol. 190, pp. 1169-1173, 2008.

[5] H. K. Choi, L. C. Burns, K. Shojania, N. Koenig, G. Reid, M. Abufayyah, G. Law, A. S.Kydd, H. Ouellette, and S. Nicolaou,”Dual energy CT in gout: A prospective validationstudy,” Annals of Rheumatic Diseases, pp. 1-6, 2012.

[6] A. Kuettner, K. Anders, and M. Lell,”Dual energy: Virtual non-contrast,” in Dual Source CTimaging, pp. 242-251, Springer, 2008.

[7] P.V. Granton, S.I. Pollman, N.L. Ford, M. Drangova and D.W. Holdsworth, ”Implementationof dual- and triple-energy cone-beam micro-CT for postreconstruction material decomposi-tion,” Medical Physics, vol. 35, pp. 5030-5042, 2008.

Duale Energie microCT voor bot-jood decompositie bijkleine proefdieren

Carmen Bouckaert

Scriptiebegeleider(s): Christian Vanhove, Stefaan Vandenberghe, Bert Vandeghinste

Abstract—In dit werk wordt de haalbaarheid van bot-jood decompositie metbehulp van van duale energie CT getest op een standaard pre-klinisch FLEXTriumph-II systeem. Veelbelovende resultaten werden verkregen door gebruikte maken van zeer simpele decompositietechnieken op een fantoom en in vivomuis data.

Kernwoorden—microCT, DECT, in vivo, muizen, materiaaldecompositie

I. INTRODUCTIE

Duale Energie Computer Tomografie (DECT) wordt steedsmeer gebruikt in de klinische praktijk. Door bij zowel lage alshoge energie een acquisitie te maken, laat deze techniek toe omop een eenvoudige manier materiaalseparatie of -kwantificatie uitte voeren, gebaseerd op de energieafhankelijkheid van de attenua-tiecoefficient [1][2]. In de klinische praktijk bestaan er reeds ver-schillende toepassingen van DECT, zoals duale energie CT angi-ografie (DE-CTA) [3], kwantificatie van nierstenen [4], detectievan jicht [5] of virtuele non-contrast beelden [6].Ondanks de vele klinische toepassingen, is de translatie van dezetechniek naar pre-klinische systemen nog niet grondig onderzocht.Het doel van dit werk is daarom om na te gaan of in vivo en exvivo scheiding van bot en jood mogelijk is, gebruik makend vaneen standaard FLEX Triumph-II systeem (Gamma-Medica Ideas,Northridge, CA, USA), aangezien microCT beelden inherent on-derworpen zijn aan een hogere hoeveelheid ruis.Toepassingen van pre-klinische DECT kunnen gevonden wordenin cardiovasculaire studies waar het belangrijk is om de verschil-lende vasculaire structuren te scheiden van de omringende niet-vasculaire structuren zoals bot.

II. MATERIALEN EN METHODEN

A. CT Acquisities

Het FLEX Triumph-II systeem werd gebruikt om de microCTbeelden te verkrijgen. Dit systeem bevat een X-stralen buis meteen aluminium filter van 2mm en een aanpasbare focale spot.De CT beelden zijn verkregen door gebruik te maken van 1024projecties, gedetecteerd door een 2368 x 2240 detector met eenpixelgrootte van 0.05mm. De duur van een acquisitie bedraagtongeveer twintig minuten. De microCT data werden vervolgensgereconstrueerd naar een 5123 voxel ruimte met een voxel groottevan 0.04mm voor het fantoom en een voxel grootte van 0.1mmvoor de muizen.

B. Optimale Energie Combinatie

Vooraleer het materiaal decompositie algoritme toe te passen,werd de optimale energie combinatie bepaald. Hiervoor werdeen densiteits calibratie fantoom (GE medical systems, London,Ontario, Canada) gescand samen met verschillende joodconcen-traties bij piek spanningen gaande van 40 kVp tot 90 kVp. Dejoodconcentraties varieerden van 25mgI/ml tot 55mgI/ml, hier-naast werd ook nog pure contraststof met een concentratie van320mgI/ml gebruikt. Negen verschillende energiecombinatieswerden beschouwd en er werd geen additionele filtratie aan detube toegevoegd. Op de gereconstrueerde beelden werden telkensregions of interest (ROI) getekend over de verschillende materi-alen en in deze gebieden werden de gemiddelde waarden en destandaardafwijkingen berekend. Vervolgens werd de relatie tussende gemiddelde waarden van een laag en hoog energetisch beeldbepaald en getekend. Fig. 1 geeft een lineare trendlijn door deverschillende joodconcentraties weer, hetzelfde geldt voor bot enzacht weefsel.

Fig. 1. 50 -90 kVp energie koppel. De groene lijn verbindt de verschillende joodconcentraties, terwijl de rode lijn zacht weefsel met bot verbindt. De groen enrood gekleurde gebieden geven het 95% betrouwbaarheidsinterval weer.

C. Decompositie Algoritme

Na de beeldreconstructie kan een materiaalseparatie algoritmetoegepast worden. Het drie-materiaaldecompositie algoritme ver-onderstelt dat de som van de volume fracties van elk van de basis-materialen in een voxel gelijk is aan een (Vgl 1) [7]. Als basisma-terialen werden bot, jood en zacht weefsel gekozen.

f1 + f2 + f3 = 1 (1)

Hiernaast kan de lineaire attenuatiecoefficient van een voxel ineen CT scan uitgedrukt worden als een lineaire combinatie vande drie basismaterialen vermenigvuldigd met hun respectievelijkevolumefracties. Voor DECT geeft dit aanleiding tot vgl. 2 en3, waarin E1 en E2 de gemiddelde energieen van de gebruiktespectra voorstellen.

µ1,E1 · f1 + µ2,E1 · f2 + µ3,E1 · f3 = µE1 (2)

µ1,E2 · f1 + µ2,E2 · f2 + µ3,E2 · f3 = µE2 (3)

Deze drie vergelijkingen leveren drie onbekenden f1, f2 and f3op, aangezien de attenuatiecoefficienten van de verschillende ba-sismaterialen kunnen gevonden worden door de gemiddelde waar-den bekomen met het densiteits calibratiefantoom te gebruiken.De waarden voor f1, f2 and f3 kunnen dan berekend worden doorvgl. 1, 2 en 3 op te lossen met behulp van een niet-negatieve klein-ste kwadraten methode. Als bijkomende voorwaarde wordt er ver-ondersteld dat alle voxels met een HU onder die van zacht weefselgeen basismateriaal bevatten en dus ook niet in rekening gebrachtworden voor de berekeningen.Om de juiste werking van het algoritme te garanderen, werd hetdierenbed dat koperen draden bevat verwijderd van de beelden.

D. In vivo en ex vivo studies

Om de mogelijkheden van pre-klinische DECT materiaalde-compositie na te gaan, werden enkele in vivo en ex vivo stu-dies met muizen uitgevoerd. De ethische commissie van de Uni-versiteit van Gent heeft alle dierenproeven goedgekeurd (ECD12/24). Vijf dieren werden verdoofd met isofluraan, vervolgensgeınjecteerd met 0.010ml/g Fenestra VC-131 R© (Advanced Re-search Technologies Inc., Saint Laurent, Canada) en, na 15 minu-ten wachten, gescand in vivo. De dieren werden eerst gescand op90 kVp, gevolgd door een scan op 50 kVp. Na het beeindigen van

Fig. 2. Validatie van het materiaaldecompositie algoritme op het fantoom. a. Origi-neel beeld b. Bot beeld (f1) c. Jood beeld(f2) d. Zacht weefsel beeld(f3)

Fig. 3. Resultaten van de materiaaldecompositie toegepast op de in vivo scans. a.Origineel beeld b. Bot beeld (f1) c. Jood beeld(f2) d. Zacht weefsel beeld(f3)

deze scans, werden de dieren geeuthanaseerd met behulp van eenoverdosis pentobarbital. Vervolgens werden de krengen ex vivogescand, gebruik makend van dezelfde acquisitieparameters.

III. RESULTATEN

A. Optimale Energie combinatie

Fig. 1 geeft een van de negen beschouwde energie combinatiesweer. De optimale energie combinatie werd geselecteerd op basisvan de hoeveelheid afscheiding tussen beide rechten en de nauw-keurigheid van de fit op de gemeten data. Gebaseerd op een vi-suele analyse van deze criteria, werd besloten dat het 50 - 90 kVpkoppel de beste resultaten opleverde. Deze combinatie werd danook gebruikt voor verdere metingen. De exposietijd per projectiewerd aangepast om een gelijke contrast-ruisverhouding te beko-men in de hoog en laag energetische beelden.

B. Fantoom

Gebaseerd op de vorige resultaten werd het materiaaldecompo-sitie algoritme eerst getest op het densiteits calibratiefantoom. Deresultaten zijn weergegeven in fig. 2. Alle joodconcentraties wer-den correct gedetecteerd als jood. Ook bot werd afgescheiden, alwerden ook de randen van de jood flacons (glas) gezien als bot.

C. In vivo en ex vivo testen

De resultaten van de materiaaldecompositie toegepast op de invivo en ex vivo beelden kunnen teruggevonden worden in fig. 3 enfig. 4, respectievelijk. We hebben ons gericht op een snede doorhet hart, aangezien het daar zeer duidelijk is waar het jood zich zoumoeten bevinden. Dezelfde snedes werden onderzocht in zowelde in vivo als de ex vivo beelden. Op het jood selectieve beeldkunnen de hartventrikels onderscheiden worden. Er is echter nogsteeds wat bot aanwezig in deze beelden, tegelijkertijd vertonenook de bot selectieve beelden een jood bijdrage.

Fig. 4. Resultaten van de materiaaldecompositie toegepast op de ex vivo scans. a.Origineel beeld b. Bot beeld (f1) c. Jood beeld(f2) d. Zacht weefsel beeld(f3)

IV. DISCUSSIE

Alhoewel de resultaten aantonen dat materiaaldecompositievoor bot en jood mogelijk is gebruik makend van dit pre-klinischsysteem, zullen meer geavanceerde technieken nodig zijn voor dein vivo data.Fig. 2 toont aan dat de materiaaldecompositie in het fantoom vrijnauwkeurig is, op het verkeerd detecteren van glas na. Dit wordtechter niet gezien als een probleem, aangezien er onder normaleomstandigheden geen glas aanwezig zal zijn in het lichaam vanhet dier.Toepassing van de materiaaldecompositie op de in vivo beelden,levert minder goede resultaten op (Fig. 3). Niettemin kunnen wenog steeds beide hartventrikels, die jood bevatten, onderscheiden.Verder kan ook gezien worden dat de scheiding tussen bot en joodniet perfect is.Hoewel gelijkaardige of betere resultaten verwacht worden voorde ex vivo scans, lijkt de materiaaldecompositie veel slechter tezijn. Een mogelijke verklaring is het ontspannen van de spierengevolgd door verstijving na de euthansie. Dit kan leiden tot be-weging van de organen en beenderen in de laag energetische scanvergeleken met de hoog energetische scan, wat correcte materiaal-decompositie kan verhinderen.

V. CONCLUSIE

We hebben aangetoond dat bot-jood decompositie mogelijk is,gebruik makend van de data verkregen met een standaard FLEXTriumph-II systeem. Doordat de decompositie nog niet perfect isgebruik makend van deze simpele techniek, zal verder onderzoeknaar meer complexe, op ruwe data gebaseerde algoritmes nodigzijn en dient het aanbrengen van extra filtratie overwogen te wor-den om meer scheiding tussen de spectra te bekomen.

REFERENTIES

[1] T.R.C. Johnson, B. Krauss, M. Sedlmair and M. Grasruck, ”Material differentiation by dualenergy CT: Initial experience,”European Radiology, vol. 17, pp. 1510-1517, 2007.

[2] M. Petersilka, H. Bruder, B. Krauss, and K. Stierstorfer, ”Technical principles of dual sourceCT,”European Journal of Radiology, vol. 68, pp. 362-368, 2008.

[3] Y. Watanabe, K. Uotani, T. Nakazawa, M. Higashi, N. Yamada, Y. Hori, S. Kanzaki, T.Fukuda, T. Itoh, and H. Naito, ”Dual-energy direct bone removal CT angiography for evalu-ation of intracranial aneurysm or stenosis: Comparison with conventional digital subtractionangiography,”European Radiology, vol. 19, pp. 1019-1024, 2009.

[4] N. Takahashi, R. P. Hartman, T. J. Vrtiska, A. Kawashima, A. N. Primak, O. P. Dzyubak, J.N. Mandrekar, J. G. Fletcher, and C. H. McCollough, ”Dual-energy CT iodine-subtractionvirtual unenhanced technique to detect urinary stones in an iodine-filled collecting system:A phantom study,”American Journal of Roentgenoloy, vol. 190, pp. 1169-1173, 2008.

[5] H. K. Choi, L. C. Burns, K. Shojania, N. Koenig, G. Reid, M. Abufayyah, G. Law, A. S.Kydd, H. Ouellette, and S. Nicolaou,”Dual energy CT in gout: A prospective validationstudy,”Annals of Rheumatic Diseases, pp. 1-6, 2012.

[6] A. Kuettner, K. Anders, and M. Lell,”Dual energy: Virtual non-contrast,ın Dual Source CTimaging, pp. 242-251, Springer, 2008.

[7] P.V. Granton, S.I. Pollman, N.L. Ford, M. Drangova and D.W. Holdsworth, ”Implementationof dual- and triple-energy cone-beam micro-CT for postreconstruction material decomposi-tion,”Medical Physics, vol. 35, pp. 5030-5042, 2008.

Contents

Overview vi

1 Introduction 1

2 X-rays 3

2.1 X-ray production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.1.1 Bremsstrahlung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.1.2 Characteristic X-rays . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.1.3 X-ray spectrum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2 X-ray interactions with matter . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2.1 Rayleigh scatter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.2.2 Photoelectric effect . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.2.3 Compton scatter . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.2.4 Pair production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

3 Computed Tomography 9

3.1 Principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3.2 Image acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3.3 Image reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.4 Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

4 Dual Energy Computed Tomography 13

4.1 Principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

4.2 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

xi

CONTENTS xii

4.2.1 Post-reconstruction methods . . . . . . . . . . . . . . . . . . . . . . 16

Two material decomposition . . . . . . . . . . . . . . . . . . . . . . 17

Three material decomposition . . . . . . . . . . . . . . . . . . . . . 17

Linear mixed images . . . . . . . . . . . . . . . . . . . . . . . . . . 18

4.2.2 Pre-reconstruction methods . . . . . . . . . . . . . . . . . . . . . . 19

4.3 Clinical applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

4.3.1 Virtual non-contrast . . . . . . . . . . . . . . . . . . . . . . . . . . 20

4.3.2 Bone removal CT angiography . . . . . . . . . . . . . . . . . . . . . 22

4.3.3 Detection of gout . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

4.4 Translation to pre-clinical systems . . . . . . . . . . . . . . . . . . . . . . . 24

5 Contrast Agents 25

5.1 Iodine based contrast agents . . . . . . . . . . . . . . . . . . . . . . . . . . 25

5.2 Gold based contrast agents . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

6 Materials and Methods 27

6.1 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

6.1.1 Acquisition and processing . . . . . . . . . . . . . . . . . . . . . . . 27

6.1.2 Phantoms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Density calibration phantom . . . . . . . . . . . . . . . . . . . . . . 28

Wire phantom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Low contrast phantom . . . . . . . . . . . . . . . . . . . . . . . . . 28

Iodine quantification phantom . . . . . . . . . . . . . . . . . . . . . 28

EDEC calibration phantom . . . . . . . . . . . . . . . . . . . . . . 29

6.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

6.2.1 Optimal energy combination . . . . . . . . . . . . . . . . . . . . . 29

6.2.2 Ideal scan parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Exposure time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Number of projection angles . . . . . . . . . . . . . . . . . . . . . . 33

Energy spectrum . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

CONTENTS xiii

6.2.3 Beam Hardening Correction . . . . . . . . . . . . . . . . . . . . . . 34

6.2.4 In Vivo and Ex Vivo Experiments . . . . . . . . . . . . . . . . . . . 34

6.2.5 Material Decomposition . . . . . . . . . . . . . . . . . . . . . . . . 35

6.2.6 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

7 Results 38

7.1 Optimal energy combination . . . . . . . . . . . . . . . . . . . . . . . . . . 38

7.2 Ideal scan parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

7.2.1 Exposure time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

7.2.2 Projection angles . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

7.2.3 Energy spectrum . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

7.3 Beam Hardening Correction . . . . . . . . . . . . . . . . . . . . . . . . . . 45

7.4 In vivo and ex vivo experiments . . . . . . . . . . . . . . . . . . . . . . . . 45

7.5 Material Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

7.5.1 Two Material Decomposition . . . . . . . . . . . . . . . . . . . . . 47

Phantom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

In vivo experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

Ex vivo experiments . . . . . . . . . . . . . . . . . . . . . . . . . . 49

7.5.2 Three Material Decomposition . . . . . . . . . . . . . . . . . . . . . 50

Phantom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

In vivo experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Ex vivo experiments . . . . . . . . . . . . . . . . . . . . . . . . . . 52

7.5.3 EDEC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

Phantom measurements . . . . . . . . . . . . . . . . . . . . . . . . 53

Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

7.6 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

7.6.1 Weighted images . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

7.6.2 Virtual non-contrast . . . . . . . . . . . . . . . . . . . . . . . . . . 60

CONTENTS xiv

8 Conclusion 64

8.1 Dual energy scan protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

8.2 Material decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

8.3 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

8.4 General conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

A Ideal scan parameters: Other results 66

B In vivo and ex vivo experiments 69

B.1 In vivo results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

B.2 Ex vivo results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

Bibliography 72

List of Symbols and Acronyms

CNR Contrast to noise ratioCT Computed tomographyCTA Computed tomography angiographyDECT Dual energy computed tomographyDSA Digital subtraction angiographyEDEC Empirical dual energy calibrationFWHM Full width at half maximumHU Hounsfield unitIV IntravascularMRI Magnetic resonance imagingPET Positron emission tomographyROI Region of interestSNR Signal to noise ratioSPECT Single photon emission computed tomography

b Basis function EDECc Calibration coefficients EDECf Volume fractionf(r) Reconstructed image of the material selective datah Plank constantme Electron massp Material specific raw dataq Polychromatic raw datar Spatial location in mediumt(r) Template imagew Weighting factorw(r) Weighting image

A Mass numberB ImageC ContrastD Decomposition functionE EnergyI Beam intensityL Material thicknessNA Avogadro numberZ Atomic number

xv

CONTENTS xvi

µ Attenuation coefficientµρ

Mass attenuation coefficient

ν Frequencyσ Standard deviationς Cross section

Chapter 1

Introduction

Dual-energy computed tomography (DECT) is a newly emerging technique in the field ofcomputed tomography imaging. It can be considered as an improvement of the currentcomputed tomography scans, as it allows the user to obtain improved material discrim-ination, giving a solution for the lack of soft tissue contrast. This lack of soft tissuecontrast is nowadays still one of the major disadvantages of CT. Apart from this, DECTalso allows material separation, giving the possibility to easily visualise the blood vesselscontaining a contrast agent without the interference of, for example, bone.

At this moment, the clinical applications of DECT have been proven and therefore thetechnique is slowly being integrated in clinical practice. Nevertheless, its pre-clinical ap-plications have not been investigated thoroughly. It could be useful to translate preclinicaltechniques to the clinical practice. Hence, the aim of this study is to examine whetherthe dual-energy technique can be applied in the small animal field as it is being appliedin the clinical practice. More in particular, the feasibility of bone-iodine decompositionwill be investigated.

In this work, first an introduction is given on the interactions of X-rays with material,after which the principle of computed tomography is explained for clinical and pre-clinicalsystems. Next, we make the extension to dual-energy CT, explaining its principle, and therelevance of the proposed research is shown. Then, a short overview of contrast agents formicro-CT is given. This is followed by a discussion of the different materials and methodsthat will be used.

Before applying dual-energy techniques on the images, the optimal settings for dual-energy imaging on the standard FLEX Triumph-II scanner, available in the INFINITYlab, must be investigated. For these measurements, the standard 2 mm aluminium fil-tration is used. First, the optimal energy combination for bone-iodine separation will bedetermined. Once this combination is known, the exposure time and number of projec-tion angles are adjusted for these energies and the image quality will be evaluated usingthe signal to noise ratio (SNR), contrast to noise ratio (CNR) and the spatial resolution.Finally, the presence of beam hardening will be evaluated and, if necessary, a simple beamhardening correction will be performed.

1

CHAPTER 1. INTRODUCTION 2

After this step, we will perform some dual-energy tests. Different material decompo-sition algorithms will be tested and optimised on phantoms. The same algorithms willalso be used on in vivo and ex vivo data to validate if the same techniques are also ade-quate for use on real mouse data. Finally, the feasibility of some applications of DECTwill be investigated.

Chapter 2

X-rays

X-rays are the best known type of ionising radiation due to its many applications inmedical and non-medical imaging. They can be considered as a type of electromagneticradiation with a wavelength ranging from 0.01 until 10 nm. X-rays are often confusedwith gamma rays. Even though both are consisting of photons, there is a slight differencein the way they are produced. While X-rays originate from electron interaction processeswith matter, gamma rays are produced within an atom’s nucleus because of electron dis-integrations from an excited state to a lower energy level.

In this chapter, the production of X-rays will first be addressed. Next, the different in-teraction mechanisms of X-rays and, more general, photons are being discussed. Finally,the clinical applications of X-rays are considered.

2.1 X-ray production

In medical practice, X-rays are mostly produced using an X-ray tube (sometimes alsocalled a Rontgen tube). A schematic overview of such an X-ray tube is shown in figure2.1. To produce X-rays, a high voltage is applied between the anode and cathode. Ascan be seen on the figure, the cathode consists of a tungsten filament, while the anodeconsists of a tungsten target. The filament is heated by allowing a current to flow throughit, which results in the release of electrons. This current of electrons is expressed in mA.The higher the temperature, the more electrons will be released. These electrons are thenaccelerated in a high voltage field towards the anode. When a voltage of 80 kV is appliedbetween the anode and cathode, the electrons will have gained an energy of 80 kV whenreaching the target. Next, the electrons collide on the target resulting in the productionof X-rays through the processes of Bremsstrahlung and characteristic X-rays. As only onepercent of the electrons is converted in to X-rays, a lot of heat is released. Therefore, arotating target is often used to allow better heat dissipation and sometimes even an oilcooling system is applied. The whole assembly is placed in vacuum to avoid electronsfrom being absorbed or attenuated due to collision with other molecules while travellingfrom cathode to anode. It also avoids early failure of the filament. In figure 2.1, it canalso be seen that the anode is inclined over a certain angle. X-rays are generated withBremsstrahlung and are emitted under an angle of 60− 90◦ compared to the direction of

3

CHAPTER 2. X-RAYS 4

Figure 2.1: Schematic overview of an X-ray tube [3]

the impinging electrons. If the anode would not be inclined, a lot of these X-rays wouldbe absorbed in the target material itself, thus lowering the output. The X-rays leave theRontgen tube through a diaphragm which limits the area of the patient that is irradiated.Next to this, there is also a filter present. This filter will cut off the low energy X-raysthat will otherwise be absorbed in the patient, resulting in a decreased radiation burdenfor the patient. At the same time, this filter will also reduce beam hardening artefacts.Beam hardening is the effect where the average energy of the input spectrum is lowerthan the average energy in the output spectrum. This effect takes place because morephotons are absorbed at low energies than at higher energies. [1][2]

2.1.1 Bremsstrahlung

Bremsstrahlung or braking radiation arises when high energy electrons impinge on amaterial with high atomic number Z, for example tungsten. When the electrons areentering the material, they are bent because of the Coulomb forces exerted by the nuclei.This results in a loss of energy which is sent out as X-rays. The amount of energy lossdepends on the distance to the nucleus. The closer to the nucleus, the higher the energyloss will be. Therefore, the bremsstrahlung process results in a continuous spectrum ofX-rays ranging from zero to the energy of the electron. The probability with which thisprocess occurs is shown in equation 2.1. In this equation, E is the energy of the electronsand Z is the atomic number of the target material.

Bremsstrahlung

Characteristic X − rays=EZ

800(2.1)

This expression shows that only 1% of the electrons are converted into X-rays, whilethe other 99% are dissipated as heat. This heat results from the fact that the interactingelectrons do not transfer enough energy to the outer shell electrons to ionise them. It onlysets them in vibration leading to the production of heat. Therefore, the target shouldhave a high melting point and a good heat conductivity.

CHAPTER 2. X-RAYS 5

2.1.2 Characteristic X-rays

A second mechanism of X-ray production, next to Bremsstrahlung, is the production ofcharacteristic X-rays. These X-rays are material specific and have a well-defined energy.When the electrons interact with the material, they can ionise the target material whichresults in vacancies. These vacancies are then filled with electrons from a higher energylevel and the difference in binding energy is then emitted as X-rays.

2.1.3 X-ray spectrum

A combination of these two phenomena explains the characteristic shape of the X-rayspectrum produced by an X-ray tube, as shown in figure 2.2. This spectrum is a superpo-sition of the continuous bremsstrahlung spectrum with discrete characteristic peaks. Asseen in this figure, the part of the X-ray spectrum generated by bremsstrahlung, changesafter leaving the X-ray tube, because the low energy X-rays are partially absorbed in thematerial of the tube housing and a filter is applied.

Figure 2.2: Typical X-ray spectrum

2.2 X-ray interactions with matter

The different interactions of X-rays with matter is the principle on which most of themedical imaging techniques are based. These interactions can be divided into two maingroups. A first group is the attenuating interactions containing photon scatter. Thisresults in an overall decrease of beam intensity when the X-rays are passing through thematerial. A second group is the mechanisms leading to photon absorption such as thephotoelectric effect. This means the X-rays deposit their energy inside the patient asionisations and excitations. An overview of the different interaction processes is given infigure 2.3(a) and they are discussed more in detail in the following paragraphs.

For mono-energetic X-rays the attenuation can be expressed using the Beer-Lambert lawgiven in equation 2.2. In this equation µ is the attenuation coefficient characteristic foreach type of material, I0 is the initial intensity of the X-ray beam, I is the intensity of thebeam after passing through the material, L is the thickness of the material and r denotes

CHAPTER 2. X-RAYS 6

(a) Different interaction mechanisms of X-rays withmatter [4]

(b) Dominant region of the different X-ray interac-tion mechanism [5]

Figure 2.3: X-ray interaction mechanisms

a certain location in the medium. The intensity measured after a beam has traversed amedium is thus dependent on all of the materials through which the beam has passed.

I = I0e−

L∫0

µ(r)dr(2.2)

Nevertheless, this equation is only valid for mono-energetic X-rays. As it was shown insection 2.1.3 that an X-ray spectrum is always poly-energetic, an adjustment needs to bemade to equation 2.2. We can express the attenuation coefficient in function of energyusing the mass attenuation coefficient as shown in equation 2.3. In this equation, ςtot is thetotal cross section for a certain X-ray energy containing contributions from all interactionsas seen in equation 2.4. In the following sections an approximate expression for each ofthese contributions will be given, but an overall trend is that the attenuation coefficientwill decrease with increasing energy (figure 2.3(b)). Therefore, higher energy photons willpenetrate material much easier resulting in less noise, but at the same time the contrastin the medical images will be decreased. A trade-off between both is therefore needed.

µ(E) =NAρςtot(E)

A= ρ

µ

ρ(E) (2.3)

ςtot(E) = ςr(E) + ςpe(E) + ςc(E) + ςpp(E) (2.4)

The different interaction mechanisms will now be discussed more in detail. [2], [6]

2.2.1 Rayleigh scatter

Rayleigh scatter or coherent scatter, is the type of scatter that occurs when a low energyX-ray interacts with a strongly bound electron. This results in the vibration of the electron

CHAPTER 2. X-RAYS 7

at the radiation frequency. When the electrons return to their resting state, photons areemitted that have the same wavelength as the interacting ones. Therefore, no energy losshas occurred. This scatter does not contribute to the image, it only causes a uniform blur.The probability for Rayleigh scatter (ςr) is given in equation 2.5. It gives an importantscatter contribution for energies below 50 keV.

ςr ∼ Z2 (2.5)

2.2.2 Photoelectric effect

The photoelectric effect is the complete transfer from the energy of the photon to one ofthe orbital electrons of the atom. Generally this occurs in an orbital shell close to thenucleus. Because of the energy transfer, the electron is ejected, the atom becomes ionisedand the photon disappears. The ionisation leads to a vacancy in the electron shell, whichis quickly filled with an electron from a higher energy level. This results in the emission ofcharacteristic X-rays with an energy Ep equal to the difference between the energy of theinitial photon (hν) and the binding energy of the electron (Ebind), as shown in equation2.6.

Ep = hν − Ebind (2.6)

The energy of these secondary photons is quite low and therefore these are completelyattenuated within the patient and do not have any influence on the imaging.

The photoelectric effect is dominant for energies below 100 keV and for materials with ahigh Z-value as seen in equation 2.7. An important fact to mention is that this cross sec-tion acts as a resonance structure each time the photon energy equals the binding energyin a certain electron shell. This results in the presence of peaks at certain energy levels,also called K-edge peaks (figure 2.4).

ςpe ∼Z4

E7/2p

(2.7)

2.2.3 Compton scatter

The Compton effect or incoherent scatter is equivalent to Rayleigh scatter, with the differ-ence that an energy loss does occur for incoherent scatter. The impeding photon interactsas a particle with an orbital electron. During this interaction, part of the photon’s energyis transferred to the electron, which gains enough energy to leave the atom. This electroncan in turn further ionise other atoms. At the same time, the scattered photon leaves theatom having a lower energy and a different direction. The energy of the scattered photoncan be calculated with the expression given in equation 2.8. From this expression, we canderive that the largest energy transfer takes place in the case of backscatter through anangle of 180◦.

Escp =

Eip

1 +Ei

p

mec(1− cos θ)

(2.8)

CHAPTER 2. X-RAYS 8

Figure 2.4: Photoelectric cross section of lead [7]

The Compton effect is the dominant effect for energies below 10 MeV and is therefore thedominant effect causing noise in X-ray diagnostics. The probability of interaction is givenin equation 2.9 and is proportional to the atomic number Z, because Compton interactioncan occur with each of the Z electrons of the atom.

ςc ∼Z√Ep

(2.9)

2.2.4 Pair production

An interaction mechanism less important for X-ray diagnostics is pair production. Duringthis interaction, the photon disappears and its energy is transformed in a positron-electronpair. This effect can only occur for energies larger than 1022 keV, twice the electron restenergy, explaining its irrelevance for medical diagnostics. The created positron will travela certain path length and will then annihilate with an electron resulting in two 511 keVphotons travelling in opposite directions. The probability for pair formation is given by:

ςpp ∼ Z2 logEp (2.10)

Chapter 3

Computed Tomography

Computed tomography (CT) is nowadays a well-established medical imaging technique.The principle used for computed tomography was invented in the beginning of the twen-tieth century by Radon. Nevertheless, it took researchers until the second half of thatcentury to develop the first computed tomography scanner. The first commercial applica-tion was the CT-scanner built by Hounsfield and Cormack in the early 1970s. Currently,the computed tomography technique is a widespread medical imaging technique, but it isalso used in the industrial environment to perform e.g. quality control or luggage inspec-tion in airports. [1]

In this chapter, the basic principles of CT will be explained, together with the differentmethods to acquire images, some reconstruction algorithms, the extension to pre-clinicalCT and the problems inherently connected with this technique.

3.1 Principle

As mentioned in the previous chapter, X-rays are attenuated when traversing matter.This attenuation differs depending on the type of material traversed and the final intensitydepends on all the materials through which the X-ray has passed. An overview of themass attenuation coefficients of materials useful for medical imaging and their energydependence is depicted in figure 3.1. The attenuation coefficients can then be obtained bymultiplying the mass attenuation coefficients with the density of the different materials.

CT-imaging is based on the principle that, by acquiring images at different projectionangles and using a reconstruction algorithm, a three-dimensional pixel map of attenuationcoefficients in the patient’s body can be obtained. This map of attenuation coefficients isrepresented in Hounsfield units (HU), defined in equation 3.1. This scale thus normalisesthe attenuation coefficients to those of water, meaning water has a HU-value of zero,while air has a HU-value of -1000. The advantage of this scale is that small differencesin tissue attenuation are seen more easily, since the grey values can be mapped onto acertain region of the scale, called windows. [8]

9

CHAPTER 3. COMPUTED TOMOGRAPHY 10

Figure 3.1: Attenuation coefficients of different medical relevant materials and their energydependence. [4]

HU =µ− µwµw

1000 (3.1)

3.2 Image acquisition

In clinical CT, the images are acquired using a rotating X-ray tube and detector in be-tween which the patient is positioned. In the most recent CT scanners, the X-ray beamis collimated in a cone beam with small aperture that allows to cover the complete objectin the transverse direction and to acquire multiple slices at once. This has reduced thescanning time tremendously compared to first generation systems, but it also leads toa higher amount of scatter. To reduce heat production, clinical CT systems use pulsedbeams. [1]

Compared to conventional CT, microCT systems must achieve a much higher resolution.This is necessary because the volume of the internal organs of a mammals scales with thebody weight. To obtain this higher resolution, detectors with smaller pixel size are usedtogether with cone beam magnification (figure 3.2). The use of a cone beam allows fasterscanning, but also limits the axial range of the specimen that can be scanned. This canhowever be solved by a translation of the animal. The used X-ray source should fulfil somerequirements. First of all, it must have a small focal spot to limit penumbrial blurringwhich degrades the spatial resolution. Secondly, the tube should emit a high photon flux.As lower energies are used for small animal imaging, more photons are absorbed in theanimal due to the photo-electric effect and thus less photons will reach the detector. Thiswill lead to increased noise in the image and thus reduced low contrast spatial resolution.Therefore, a high photon flux is required to ensure sufficient photons reach the detector.


Finally, the X-ray tube should allow the user to select energies within a suitable range.[9][10][11]

Figure 3.2: Principle of cone beam magnification [9]

Two configurations of microCT scanners exist.

A first configuration is the rotating sample configuration. In this technique, the sourceand detector are kept stationary while the sample rotates parallel to the central axis ofthe CT system. It is important that the source and detector are precisely aligned withthis central axis. This method is however not suited for in vivo imaging since the animalmust be immobilised in upright position and must rotate around its own axis resulting intremendous organ movement. The upright position furthermore hampers the administra-tion of contrast agents and anaesthetics while scanning. [10][11]

The second configuration is the rotating gantry configuration. Here, the position of theanimal is fixed, while the source and detector are mounted in a rotating gantry. Thesource and detector are facing each other and rotate around the central axis. The animalis positioned on an animal bed which can be moved precisely using a micrometer stage.This technique is better suited for in vivo imaging because it allows easier animal handlingand has a decreased scanning time. [10][11]

3.3 Image reconstruction

The acquired data can be reconstructed using two different reconstruction techniques,namely analytical and iterative reconstruction. [1]

A first group of techniques are the analytical reconstruction methods. They calculatethe mathematical inverse of the tomography process using filtered backprojection. It ismost often used in clinical practice, due to its very fast computation time, leading toquasi real-time imaging. A modification of this algorithm, the Feldkamp cone beam re-construction algorithm, can be used in small animal imaging.

Next to the analytical techniques, iterative reconstruction methods can also be consi-dered. These methods use a model for forward and backprojection. As the image qualityimproves with each iteration, these techniques yield better image quality, but also requiremore reconstruction time. Therefore they are not used that often in clinical practice.


Nevertheless, recent advancements, like Veo developed by GE medical systems (London,Ontario, Canada), may lead to introduction of iterative reconstruction techniques in clin-ical practice in the near future.

3.4 Problems

One of the problems inherently connected to CT and microCT is the lack of soft tissuecontrast. This lack of contrast is caused by the superposition principle, an effect whichwill be discussed more in detail in the next chapter. Its basic idea is that materials withdifferent properties, e.g. bone and iodine, may have similar attenuation coefficients andthus similar grey values in the final CT image. Therefore it becomes very difficult todifferentiate regions containing these materials.

This problem becomes even more apparent in small animal imaging, because the lower en-ergies lead to increased noise and thus lower image quality and lower soft tissue contrast.Furthermore, the use of a cone beam leads to an increased amount of scatter between thedifferent slices, as no anti-scatter grid can be used because of the small detector pixels andthe higher magnification. Finally, because of the small detector pixels, less photons canbe detected per pixel, which results in higher photon noise. All of the remarks discussedhere, will make material differentiation in small animal imaging even more cumbersome.Therefore, a lot of research is being performed to search for a means to increase the abi-lity to differentiate between materials. One of these solutions may be dual energy CT, atechnique that will be discussed more in detail in the next chapter.

Chapter 4

Dual Energy Computed Tomography

A possible solution for the lack of material differentiation in conventional CT can be foundin the field of dual energy computed tomography (DECT). The ideas for this techniquewere already founded in the late 70s, but clinical applications were hampered because ofsome initial implementation problems. CT scanners in the 70s were prone to unstableHounsfield unit numbers and thus introduced errors in the reconstruction. Furthermore,patients had to be scanned twice and scan durations were much longer than they are now,resulting in motion artefacts. A third major problem was that X-ray tubes at the timewere not able to deliver sufficient tube currents at low voltages. This results in differentnoise properties for the different kVp values preventing quantitative data evaluation. [12],[13]

Thanks to recent technological improvements, the aforementioned problems have beeneliminated, and new interest is shown in DECT technology and its numerous applica-tions.

4.1 Principle

The technique of DECT is based on the energy dependence of the attenuation coefficient,as was discussed in paragraph 2.2. It poses a solution for one of the major problems ofconventional CT, namely the lack of material differentiation capabilities.

This lack of contrast can be explained using the superposition principle. This princi-ple is graphically represented in figure 4.1. In this figure, two materials are considered.

Material 1 has a low mass density ρ1 and a high mass attenuation coefficient(µρ

)1, while

material 2 has a high mass density ρ2 and a low mass attenuation coefficient(µρ

)2. The

total attenuation of a photon beam through a material is defined by the attenuation co-efficient, which is the product of the mass density and the mass attenuation coefficient.

It is therefore possible that two materials, with ρ1 < ρ2 and(µρ

)1>(µρ

)2, result in the

same final attenuation of the beam, and are thus represented by the same grey value inthe final image. This effect is called the superposition principle.

13

CHAPTER 4. DUAL ENERGY COMPUTED TOMOGRAPHY 14

Figure 4.1: Superposition principle

Compared with conventional CT, DECT uses two different peak X-ray energies to acquirethe images instead of just one peak energy. By using these different X-ray energies, itcan rely on the energy dependence of the attenuation coefficient to differentiate betweenmaterials that would otherwise be superimposed. This is represented in figure 4.2. Thisfigure was generated using the attenuation values calculated with XCOM [7]. The massattenuation values for bone, iodine, fat and muscle are represented in the useful energyrange for diagnostic imaging. The two vertical lines represent the mean photon energiesof the 80 and 140 kVp spectra used in clinical DECT [13]. It can be seen in this figurethat the mass attenuation coefficient of bone differs more from that of muscle at 53.3 kVpthan at 71 kVp. The same argument is valid for the differentiation between muscle andfat.

4.2 Implementation

There are different methods that can be used to acquire the images used for DECT.

The first technique, sequential scanning, is the most straightforward. It scans the subjecttwice using two different tube voltages. This results in images at all projection anglesallowing full reconstruction for both energies. Apart from this, this technique can also beapplied using a regular (micro)CT scanner, requiring no modifications. However, a draw-back is the possibility of movement artefacts because the patient needs to be scannedtwice. This will degrade the final result, which can only be solved partially by motioncorrection. Each detected photon ray will have been attenuated by slightly differentanatomical regions, leading to a mismatch in the measured data.

The second technique, dual source acquisition, requires a modification of the CT scannerand is represented in figure 4.3(a). This technique is implemented by Siemens for theirclinical scanners and uses two X-ray sources positioned over an angle of approximately90◦. As can be seen in figure 4.3(a) both detectors cover different measurement fields.Detector A covers a field of 50 cm, while detector B covers a field of 26 or 33 cm. Thisconfiguration is chosen to keep the dimensions of the system within limits. Both sourcescan be used at the same energy level, to enhance temporal resolution useful for cardiac


Figure 4.2: Energy dependence of the mass attenuation coefficients

imaging. On the other hand, they can also be used at different energy and current levelsfor dual energy applications. As both images are acquired simultaneously, movement arte-facts are reduced. However, scatter radiation is inherently connected with this method,since the first tube induces scatter in the second detector and vice versa. Therefore,scatter correction algorithms should be applied to avoid image degradation and loss ofcontrast.[12][14]

The third technique is called fast kVp switching and is developed by GE Healthcare.It uses an X-ray source that continuously switches from high to low energies during therotation around the patient. Therefore, this X-ray source must be able to switch rapidlyfrom high to low energies and vice versa. At the same time, the detector must be able toprocess the signals fast enough to enable sufficient energy separation. Therefore a Gem-stone, a complex rare earth based oxide, is used as a scintillator material. This materialhas a decay time of only 30 ns and a low afterglow level. In order to keep the photon fluxfor both energies more or less equal, the exposure with low kVp X-ray is taken longerthan with high kVp X-rays. However, this results in images for only half of the projectionangles for each energy, so designated processing algorithms are required. [15][16]

A final technique that can be used is dual layer CT and is being developed by PhilipsHealthcare. This method uses one X-ray source and one detector that is modified suchthat the scintillator is made from two separate layers, see figure 4.3(c). These scintillatorscan be read out separately. When the detector is irradiated with high kVp X-rays, the toplayer absorbs most of the low energy photons, thus generating a low energy image. Thebottom layer on the other hand, absorbs most of the remaining higher energy photonsresulting in a high energy image. The advantage of this method is that only one exposure


of the patient with high energy X-rays is needed, thus reducing the radiation dose. Fur-thermore, since both images are acquired simultaneously on the same detector element,there is no time lag between the different projections and both images are thus alignedspectrally and temporally. Finally, it also eliminates the cross scatter that is present usingdual source CT. However, this technique is not yet available commercially. [17]

(a) Dual source acquisition [12] (b) Fast kVp switching [15]

(c) Dual layer CT [14]

Figure 4.3: DECT image acquisition techniques

After acquiring images at different projection angles, they can be processed either beforeor after the image reconstruction. It is thus possible to distinguish pre-reconstructionmethods and post-reconstruction methods.

4.2.1 Post-reconstruction methods

Image based processing methods or post-reconstruction methods consider the raw datasets qL and qH as being independent until they are reconstructed. After the reconstruction,the properties of the images can be combined to obtain material selective images. Theseimage based techniques are however approximate techniques as beam hardening effectsare not removed and the resulting images are somewhat less quantitative. In the followingparagraphs an overview of some common image based methods is given. [18]


Two material decomposition

Two material decomposition or material labelling is a simple technique that can onlybe used for very specific decompositions e.g. the decomposition of bone and iodine orthe decomposition of calcium and uric acid. This method is based on the relationshipbetween the high and low energy CT values for specific materials as the mixtures oftwo non-interacting materials are always located on a straight line. When mapping thevalues from different densities of these materials on a CT-value diagram, we obtain twodistinct lines, one for iodine and one for bone. A graph as shown in figure 4.4 is thenobtained. The easiest method to separate the two materials is by drawing a bisectorbetween both lines. The ideal separation line may however deviate somewhat from thisbisector depending on image noise and material concentrations. A voxel is then classifiedas iodine when its CT-values are found in the region between the separation line and theiodine line, similarly, a voxel is classified as bone when its CT-values are found in theregion between the separation line and the bone line. Furthermore, it is important tointroduce a minimum threshold on the voxel values to avoid that voxels containing onlymuscle are also classified as bone or iodine. Using this method, the material selectivevoxels may then be represented separately to obtain material selective images. [14][19]

Figure 4.4: CT-value diagram used for material labelling. The upper green line denotes theiodine mixtures while the lower red line denotes the bone mixtures. The blue line in the centre

is the separation line and the purple line denotes the threshold

Three material decomposition

Different types of three material decomposition algorithms exist. These algorithms de-termine the mass or volume fractions of three materials in a mixture. Here, we will onlydiscuss the algorithm that will be used in the remainder of this work. [20][21]

The algorithm described here was developed specifically for cone-beam microCT. It as-sumes that the sum of the volumes of the three materials is equivalent to the volume ofthe mixture. This is expressed in equation 4.1 using the volume fractions of the three


materials, expressed by f1, f2 and f3. Furthermore, the linear attenuation coefficient µin a voxel can be expressed as a linear combination of the attenuation coefficients of thebasis materials multiplied by their volume fraction [21]. As we acquire scans with twodifferent spectra with mean energies E1 and E2, this results in equations 4.2 and 4.3.

f1 + f2 + f3 = 1 (4.1)

µE1 = µ1,E1f1 + µ2,E1f2 + µ3, E1f3 (4.2)

µE2 = µ1,E2f1 + µ2,E2f2 + µ3, E2f3 (4.3)

This yields three equations for the three unknowns f1, f2 and f3. The linear attenuationcoefficients for the basis materials µ1, µ2 and µ3 can be measured from the CT images bydrawing regions of interest in the different basis materials. Furthermore, when the valueof a voxel is found below that of soft tissue, it is excluded from the calculations as it isassumed these voxels do not contain any basis material. A solution for the unknowns canthen be found using non-negative least squares methods.

Linear mixed images

Apart from material specific images, dual energy techniques can also be used to makea combination of the high and low energy data, a so called mixed image. These mixedimages can then be used for diagnostic interpretation or combined with an iodine map.Although the low or high energy images could also be used separately for diagnostic in-terpretation, their image quality is often worse than that of a single energy image.

A mixed image can be constructed using equation 4.4, in this equation BL and BH rep-resent the low and high energy image, while wL and wH are the weighting factors for thelow and high energy images. Furthermore wL + wH = 1 should be valid. [22]

B = wLBL + wHBH (4.4)

The mixed image can be optimised based on the amount of noise present (equation 4.5)or based on the contrast (equation 4.6). In these equations σi,b and σi,s are the standarddeviations of the CT numbers in the background and signal regions and Ci is the contrastfor the high and low energy images.

wL(σmin) =σ2H,b

σ2Lb

+σ2H,b

σmin =σL,bσH,b√σ2L,b+σ

2H,b

(4.5)

wL(CNRmax) =CL(σ

2H,s+σ

2H,b)

CL(σ2H,s+σ

2H,b)+CH(σ2

L,s+σ2L,b)

CNRmax =

√2C2

L

σ2L,s+σ

2L,b

+2C2

H

σ2H,s+σ

2H,b

(4.6)


Once these weighting factors are calculated, a trade-off should be made for better contrastor less noise. Better contrast generally means the contribution of the low energy imagewill be higher, while less noise means the contribution of the high energy image will behigher.

4.2.2 Pre-reconstruction methods

Raw data based processing methods or pre-reconstruction methods will correctly treatthe available data by passing them through a polynomial decomposition function beforereconstructing the image. The resulting reconstructed images will then be material selec-tive and will not contain any beam hardening artefacts. [18][23]

One of the pre-reconstruction methods is Empirical dual energy calibration (EDEC).Compared to some of the three material decomposition methods, EDEC does not requireknowledge of the spectra or the attenuation coefficients. At the same time no exact in-formation of the calibration phantom geometry, size and position is needed. [23]

The aim of EDEC is to find a material specific decomposition function Di which is a linearcombination of basis functions bn(q1, q2), as shown in equation 4.7. Most often polyno-mials of the form bn(q1, q2) = qk1q

l2 are chosen for the basis functions, with k = 0, ..., K,

l = 0, ..., L and n = k(L+1)+l leading to N = (K+1)(L+1) basis functions. Using thesebasis functions, the problem is reduced to finding the coefficients cin. In these equationsthe index i = 1, 2 depicts the materials in which we want the image to decompose.

pi = Di(q1, q2) =N−1∑n=0

cinbn(q1, q2) (4.7)

In order to find the coefficients c, the least square deviation given in equation 4.8 mustbe minimised. In this equation, f(r) =

∑n cnR

−1bn(q1, q2) is the reconstruction of thematerial-selective raw data written as a linear combination of the basis images. Fur-thermore, t(r) is a template image that represents the a priori knowledge of the regionscorresponding to material 1 and those that do not contain material 1, and w(r) is theweight image which is set equal to one in all voxels where we are sure of the content.

E2 =

∫d2r w(r)(f(r)− t(r))2 (4.8)

The basis images, template image and weight image are found by acquiring a DECTscan of a calibration phantom. This calibration phantom should contain homogeneousareas with sufficient amounts of material 1 and material 2 and should further assure thatall reasonable path length combinations of material 1 and material 2 are acquired. Anexample of such a calibration phantom is shown in figure 4.5. [23]


Figure 4.5: A possible calibration phantom used for EDEC [23]

4.3 Clinical applications

In the previous years, numerous applications of dual energy CT have been discovered. Ap-plications can be found in multiple disciplines e.g. virtual non-contrast images [24][25][26][27][28], bone removal CT angiography (CTA) [29][30][31][32][33][34], virtual autopsy [35],measurement of iron overload [36], determination of kidney stone composition [37][38][27],diagnosis and follow up of gout [39][40][41][42][43] and visualisation of tendons and carti-lage [44].

In the following sections, some of these applications will be discussed more in detail.

4.3.1 Virtual non-contrast

A virtual non-contrast image, is a contrast enhanced image acquired by dual energy tech-niques, where the iodine component has been removed without removing other parts ofthe image. It thus seems as if no contrast is present in this image. This technique is im-portant in clinical diagnostics as it removes the need to acquire both a contrast enhancedimage and an unenhanced image, thus lowering the dose to the patient. Often the result-ing virtual unenhanced low and high energy images can be combined in so-called weighted120 kVp equivalent images which have less noise and improved contrast. [24][25][26]

Applications of these virtual non-contrast images are imaging of renal masses, hema-turia, evaluating liver lesions, adrenal tumours and pancreatic tumours.

The identification of renal masses is based upon the enhancement of a lesion in pres-ence of a contrast agent. Currently, first an unenhanced image is acquired that is usedfor baseline density measurements of the renal masses and evaluation of calcifications andfat. Then a contrast agent is administered and the amount of enhancement in the lesionis assessed. Using DECT, the unenhanced scan can be omitted, as a virtual non-contrastimage can be generated from the enhanced scans. This image can then be used for thebasic density measurements. The resulting iodine contribution can be shown as a colourcoded overlay on the weighted virtual non-contrast image. An example of the use ofvirtual non-contrast images for the identification of renal masses can be found in figure4.6, together with a true non-contrast image. This application is especially useful when


a renal lesion is detected accidentally during a contrast enhanced CT. [25][27]

Figure 4.6: Characterisation of renal masses using DECT, the white arrow denotes the lesion.a. Weighted average contrast-enhanced CT image b. Virtual unenhanced image c. Virtual

unenhanced image with colour coded iodine overlay d. True unenhanced image [25]

Similarly, DECT can also be used to find the cause of hematuria. Hematuria may havemany causes such as renal stones, renal tumours or inflammation of the urinary tract,but there is no consensus on the most appropriate imaging technique. At the moment,CT urography protocols consist of an unenhanced scan to detect the calculi, followed byan unenhanced scan to evaluate renal parenchymas and urothelium. Again, the use ofDECT may obviate the unenhanced scan. An example of DECT used for the evaluation ofhematuria can be found in figure 4.7. However, the delineation of renal stones may be notas good for smaller stones on the virtual unenhanced images as on the true unenhancedimages. Furthermore, when high concentrations of iodine are present, the iodine removalmay be incomplete. Remainders of iodine can then be confused for calculi or obscurecalculi. [25][27]


Figure 4.7: Use of DECT for the evaluation of hematuria. a. Contrast enhanced scanrepresenting the location of contrast agent in the left kidney b. Virtual non-contrast image

representing a stone in the left kidney [25]

Current tests on virtual non-contrast show a good correlation between virtual and trueunenhanced images. However, virtual unenhanced images are often associated with ahigher level of noise. Furthermore, calcifications present in renal lesions are less obviousin the virtual unenhanced images and small amounts of fat are also more difficult todetect. Nevertheless, the use of virtual non-contrast images reduces the radiation burdenon the patients and allows for faster patient throughput. [25][27][28]

4.3.2 Bone removal CT angiography

In the field of CT angiography, direct subtraction angiography (DSA) is considered thestandard for diagnostic imaging. First a preliminary unenhanced CT acquisition is madefollowed by a contrast-enhanced CT. This is followed by a subtraction of both imagesleaving a representation of the contrast medium filled vessels only. However, a drawbackof this technique is that movement of the patient may lead to misregistration artefacts.Furthermore, this technique is associated with an increased radiation dose since two scansmust be acquired. [29][34]

Alternatively, an MRI examination may be performed. Unfortunately, this technique isnot generally applied in clinical practice because of its high cost, limited spatial resolutionand numerous contradictions. [34]

Therefore, a better alternative for conventional DSA may be found in the field of DECT.Using a dual source scanner, two energy datasets may be obtained simultaneously, avoid-ing misregistration artefacts. Using the energy dependence of these two datasets and athree material decomposition algorithm, the bone and soft tissue may be removed andonly the vessels filled with iodine remain. The result of such a subtraction may be seen infigure 4.8. Some preliminary experiments have shown that this technique yields similarresults as conventional DSA, as can also be seen in the figure. Conventional CTA on theother hand, does not show structures covered by bone. [29][32][34]

One of the advantages of this technique compared to DSA is a reduction in radiationdose of approximately 30-50%. [32] Furthermore, DE angiography allows to display orremove the calcifications of aneurysms thus allowing inspection of the wall and the lumen.This is not possible using DSA. [29]


Figure 4.8: Comparison of DE-CTA with conventional DSA and CTA. Figures a and crepresent the results of DE-DTA, while b represents a conventional CTA image and d a DSA

image [29]

A disadvantage is the presence of blooming effects. These result in the overestimationof stenosis and thus make it difficult to differentiate calcifications from iodine. However,it is expected that with further optimisation of the technique this effect might decrease.[29]

4.3.3 Detection of gout

Gout is caused by the deposition of uric acid in the distal extremities. To diagnose gout,presence of intra-articular monisodium urate crystals should be detected. In currentclinical practice this is achieved by aspiration and examination of synovial fluid usingpolarised light microscopy for the presence of these crystals. However, this joint aspirationis not always possible and is accompanied with a risk of complications. Furthermore,serum urate levels in gout patients change with time and thus do not always result in thedeposition of crystals. Therefore, a better imaging technique for the accurate diagnosisand follow-up of gout is required. [40][39][41][43]

Using DECT techniques, the presence of uric acid crystals in the tophi can be showndirectly. This is possible using a two material decomposition technique based on thechemical composition of uric acid. The presence of uric acid crystals can then be pre-sented using a colour coded overlay (figure 4.9). This results in a more sensitive andspecific diagnosis of gout together with a representation of the affected joint. Therefore,the method is able to detect the disease at a very early stage allowing earlier onset oftreatment. Furthermore, as the urate crystals are detected automatically, there is nointer-user variability present when performing volumetric quantification. This allows toquantify even very small changes in tophus volume and thus to perform easy follow-up oftreatment. [39][40][41][42][43]


Figure 4.9: Gout detection using DECT. a. Diagnostic CT b. Colour-coded composition CT,uric acid deposition is depicted in green, osseous structures are depicted in white and purple.

c. Surface-rendered 3D CT image [43]

4.4 Translation to pre-clinical systems

As the previous examples show, many applications of DECT in clinical practice alreadyexist. However, the translation to pre-clinical DECT has not been investigated thoroughly.

A first application of pre-clinical DECT may be found in cardiovascular studies, where itcan be used to separate iodine filled vessels from the surrounding non-vascular structureslike bone and soft tissue. Important to note is that the material decomposition algorithmsare able to detect smaller amounts of contrast agent and therefore DECT allows to useless contrast agent, thus lowering the load on the animal’s heart especially during thesecardiovascular studies.

Secondly, using dual energy techniques it is possible to construct mixed images thathave lower noise and better contrast than equivalent single energy images. This is espe-cially important in small animal imaging, where higher noise levels are inherently present.

Finally, as DECT is able to detect small concentrations of certain substances automat-ically, it is suited for follow up studies. Many drugs are tested on small animals beforeinitiating a clinical trial. Using DECT, simple quantitative follow-up of the response tothese drugs is possible.

Chapter 5

Contrast Agents

Due to the lack of soft tissue contrast in CT images, often a contrast agent is used. Thiscontrast agent will increase the differences in HU between the surrounding soft tissue andthe organ to be visualised. The contrast agent can be administered via diverse routes:intravascular (IV) administration to visualise blood vessels or to enhance the structureof organs, oral administration to highlight the gastrointestinal tract or rectal administra-tion to highlight the large intestine. As the tests further on in this work will focus onthe detection of a contrast agent in the vascular system, only IV contrast agents will beconsidered. In microCT, intravascular contrast agents based on iodine or gold particlesare the most common.

To determine the minimum amount of contrast necessary, the Rose condition is used.This condition states that in order to discriminate between two densities, the contrastdifference should at least be 3-5 times greater than the noise level present. For microCTthis means that a difference of approximately 150 HU between different structures isneeded to distinguish them [45].

5.1 Iodine based contrast agents

Iodine based contrast agents are most common used. In clinical practice, most oftenwater soluble contrast agents are used, however these are not suitable for small animalimaging, as acquisition times are much longer and noise levels are higher. Therefore, weneed contrast agents with longer residence times together with a higher level of enhance-ment. This may be achieved by encapsulating the iodine molecule in liposomes. Theseliposomes have longer circulation times in the blood pool and can contain multiple iodinemolecules [45]. The higher concentration of iodine will increase the enhancement, as theattenuation is proportional to the iodine concentration. More precise, each milligram ofiodine per millilitre of blood will increase the attenuation by 25 HU [46].

Iodine has a K-edge peak at 33.1 keV (figure 5.1(a)). The location of this peak is ideal toperform small animal dual energy imaging as the average energy of the spectra used liesaround this energy. Ideally, the spectra should be chosen so that the average energy ofthe low energy spectrum is found in the proximity of the K-edge peak while the average

25

CHAPTER 5. CONTRAST AGENTS 26

energy of the high energy beam is located further away from the peak. This may beachieved by adding additional filtration in the high energy beam to separate the averageenergies further.

(a) Mass attenuation coefficient of iodine as a func-tion of photon energy [7]

(b) Mass attenuation coefficient of gold as a func-tion of photon energy [7]

Figure 5.1: Mass attenuation coefficients

5.2 Gold based contrast agents

Currently, gold nanoparticles are only suited for use in small animal imaging. They havethe advantage that their absorption is larger than that of iodine, thus offering more con-trast. In addition, gold allows the formation of stable, biocompatible nanoparticles indifferent sizes. This size allows control over the biodistribution, blood pool lifetime andclearance pathway. [47]

Compared to iodine, gold has a K-edge peak that is much higher. It can be found at80.7 keV (figure 5.1(b)). The location of this peak, make gold less suitable for use insmall animal DECT. In order to reach mean energies around this value, a lot of filtrationwould be needed. This filtration would only keep some high energy X-rays in the spec-tra, thus requiring a very high tube output to reach acceptable noise levels, which is notfeasible in practice.

Chapter 6

Materials and Methods

6.1 Materials

6.1.1 Acquisition and processing

The images are acquired using the FLEX Triumph-II microCT scanner from GammaMedica-Ideas (Northridge, CA, USA, figure 6.1). This scanner is a tri-modality scannerdesigned for microCT, -PET and -SPECT applications. In the study conducted here,only the microCT function will be used. This system contains a 2 mm aluminium, 1 mmberyllium filtered tube with adjustable focal spot size. The scanner allows to change theX-rays energies in a range from 30 - 90 kVp. Simultaneously, the desired exposure timecan be set by the user. Based on the combination of the chosen X-ray energy and exposuretime, the machine software will search for the optimal current during the calibration ofthe machine. The current is chosen to keep the mAs value constant within the availablerange of currents. This leads to similar noise levels for the different energies.

The detector consists of 2368 x 2240 pixels with a pixel size of 0.05 mm. This detec-tor can be used in 1 x 1, 2 x 2 and 4 x 4 binning. In the following experiments, the 1 x 1binning is always used as at the onset of the experiments it was the only binning modethat worked properly for the desired energy ranges and exposure times.

Once the images are acquired, they are reconstructed with COBRA 7 (Exxim CC, Liver-more, CA, USA) using the Feldkamp cone beam reconstruction algorithm. The processingof the images was performed with MATLAB R© 2011b (Mathworks inc., Natick, MA, USA).

6.1.2 Phantoms

The phantoms used are a combination of standard phantoms manufactured by QRMGmbH (Inc., Moehrendorf, Germany) and self-made additions. An overview of the usedphantoms is given in the next paragraphs.

27

CHAPTER 6. MATERIALS AND METHODS 28

Figure 6.1: Gamma-Medica Triumph microCT

Density calibration phantom

This phantom was developed as a density calibration tool by GE medical systems (London,Ontario, Canada). It is a cylindrical phantom containing multiple regions with differentmaterials. A schematic overview of its construction is shown in figure 6.2(a). The phantomwe will use, contains regions with bone tissue, muscle, fat and air.

Wire phantom

This phantom is manufactured by QRM. It consists of two tungsten wires embedded inair, as shown in figure 6.2(b). One of the wires is located slightly off centre, while theother is located away from the centre. The thickness of the wires is 10µm. This phantomcan be used to perform resolution measurements for the different scanner settings.

Low contrast phantom

Like the wire phantom, the low contrast phantom is also produced by QRM. This phan-tom contains four regions of low contrast as compared to the background. A schematicoverview is given in figure 6.2(c). Two of these regions have a contrast difference of -40 HU or 4%, while the other two regions have a contrast difference of -80 HU or 8 %.This phantom can be used to determine the contrast and the contrast to noise ratio fordifferent scanner settings.

Iodine quantification phantom

This phantom is a combination of the density calibration phantom discussed before andsome capillary tubes filled with different iodine concentrations as can be seen in figure6.2(d). The meaning of the different names is clarified in table 6.1. These concentrationsare chosen because in a standard pre-clinical contrast research a maximum of 0.25 ml


contrast agent per 20 g mouse is used. Knowing the concentration of clinical iodine is320 mgI/ml and an average lab mouse weighs approximately 30 g, we obtain a maximumconcentration of 57 mgI/ml in the blood.

Name Iodine concentration [ mgI/ml]c1 55c2 50c3 45c4 40c5 35c6 30c7 25c8 320

Table 6.1: Iodine concentrations used in the phantom

EDEC calibration phantom

This phantom is composed of two cylinders of different diameters (figure 6.2(e)). Thelargest cylinder contains water, while the smaller phantom contains iodine. The concen-tration of iodine is 6 mgI/ml corresponding to the iodine concentration in the blood ofthe mice used for the in vivo and ex vivo tests.

6.2 Methods

6.2.1 Optimal energy combination

This method will determine the most suitable energy combination to differentiate betweenbone and iodine. Therefore we will scan the density calibration phantom combined withsome capillary tubes filled with different iodine concentrations as described in section6.1.2. This phantom will be scanned at different kVp settings, an overview of which isgiven in table 6.2. The exposure time is held constant. The scanner itself adjusts the tubecurrent depending on the chosen energy and exposure time in order to obtain optimal scanconditions. The images are acquired using a 1 x 1 binning and 1024 projection angles.

Energy (kVp) Exposure Time (ms) Current (µA)40 500 79050 500 79060 500 53070 500 35080 500 25090 472 200

Table 6.2: Scanner settings for the determination of the optimal energy combination


(a) Density calibration phantom (b) Wire phantom [48]

(c) Low contrast phantom [48] (d) Iodine quantificationphantom

(e) EDEC calibrationphantom

Figure 6.2: Overview of the different phantoms used

After acquiring the images at these different settings, they are reconstructed using a conebeam reconstruction algorithm with COBRA. This results in a 512 x 512 x 512 matrix witha voxel size of 0.04 mm. Next, we determine the average Hounsfield unit in the differentregions of the phantom. This is done by drawing different regions of interest (ROI) ona slice (figure 6.3), extrapolating these ROI’s over multiple slices and by calculating theaverage of the grey values in these regions. All of these operations are performed usingMATLAB R©.

After calculating the average HU and their standard deviation in the different ROI’s,we can represent these in a graph which shows the HU in a low energy image versus theHU in a high energy image.

6.2.2 Ideal scan parameters

Apart from the optimal kVp-settings, we also need to determine the optimal conditionsfor the other tunable parameters. These parameters are the exposure time and the num-ber of projection angles. Furthermore, we can also vary the amount and type of filtrationin front of the X-ray tube, resulting in a change of the energy spectrum. All of these pa-rameters can influence the final image quality and are examined using the wire phantomand the low contrast phantom. These phantoms are used to determine the signal to noiseratio, the resolution and the contrast to noise ratio of the images.


Figure 6.3: Location of the different regions of interest used for the determination of theoptimal energy pair

The signal to noise ratio is determined by drawing a circular region of interest in thebackground region of the low contrast phantom. This ROI should not be located tooclose to the edge of the phantom (figure 6.5(a)). In this ROI, the mean and standarddeviation are calculated. The signal to noise ratio is then determined as the ratio of themean and the standard deviation. The SNR is calculated in 100 slices and then averagedto remove interslice variability.

The resolution is determined by drawing a linear region of interest through the centralwire of the wire phantom after averaging over 100 slices (figure 6.5(b)). We then obtainan array of grey values which can be represented in a graph as distance or pixel numberversus grey value. On this graph we can next fit a line spread function, often followingthe course of a Gaussian function as shown in figure 6.4. From this line spread function,we can finally calculate the full width at half maximum, which is a representation of theresolution. As the spatial resolution is also dependent on the voxel size, the images willbe reconstructed with voxel sizes of 0.1, 0.05 and 0.02 mm.

Finally, the contrast to noise ratio can be calculated using the low contrast phantom. Thecontrast is determined by drawing circular ROI’s in the regions of low contrast and in thebackground region (figure 6.5(c)). In order to clearly see the low contrast regions, we firstaverage over 100 slices. Once the ROI’s are assigned, the CNR and contrast difference arecalculated in one slice. The contrast is then the difference of the average HU in the lowcontrast region and the average HU in the background region. The contrast to noise ratiois finally determined by dividing the calculated contrast value by the standard deviationof the HU in the background. As for the SNR, the CNR is calculated in 100 slices andthen averaged to remove interslice variability.

All of these values will only be calculated for the optimal energies determined in sec-tion 6.2.1.


Figure 6.4: Example of a Gaussian fit to the line spread function

(a) Signal to noise ratio (b) Spatial resolution (c) Contrast to noise ratio

Figure 6.5: Location of the regions of interest used to determine the image quality parameters

Exposure time

The exposure time will be varied as shown in table 6.3

Exposure Time (ms) Current (µA)

50kVp 80kVp 90kVp

500 790 250 190400 790 320 240300 790 430 320200 790 620 480

Table 6.3: Scanner settings for the variation of the exposure time


Number of projection angles

Starting from the scans obtained in the previous step, we will vary the number of projec-tion angles and determine its influence on the image quality. From the original images, therequired number of projection angles are selected using a MATLAB R© code. The followingnumbers of projection angles are considered: 1024, 512, 256, 128.

Energy spectrum

The energy spectrum can be changed by adding or removing filtration layers in front ofthe X-ray tube. The effect of different amounts of filtration will only be considered intheory using SpekCalc, a program that can calculate spectra theoretically developed byGavin Poludniowski and Phil Evans at The Institute of Cancer Research, London, UK[49][50][51]. An overview of the different filtration materials and their mass attenuationcoefficient as a function of energy is given in figure 6.6. By varying the amount of filtration,the aim is to obtain two X-ray spectra suitable for dual energy imaging. This means thelow energy spectrum should have an average energy below the k-edge peak of iodine orgold, while the high energy spectrum should have an average energy just above the k-edge peak. Another option is for the low energy spectrum to have an average energy justabove the K-edge and the high energy spectrum to have an average energy somewhathigher than the K-edge.

(a) Aluminium (b) Beryllium (c) Copper

(d) Tantalum (e) Tin (f) Tungsten

Figure 6.6: Attenuation coefficients of the different filtration materials available in SpekCalc


6.2.3 Beam Hardening Correction

As mentioned in chapter 2, beam hardening may introduce a lot of artefacts in the CTimages. Apart from the introduction of filters in the X-ray beam, a simple beam hardeningcorrection may also be applied in the reconstruction phase. COBRA allows us to performa simple linearisation correction. It requires the user to scan different water thicknessesand measure the attenuation. This attenuation value is calculated as the logarithm ofthe incident intensity minus the logarithm of the attenuated intensity. Representingthese attenuation values as a function of thickness, a curve deviating from the expectedstraight line is obtained (figure 6.7). To perform beam hardening correction, a third orderpolynomial should be fitted to these measured data and the obtained coefficients entered inCOBRA. COBRA will then calculate the required correction factors and apply the beamhardening correction to the data. According to the COBRA manual, the coefficients areexpected to be a0 ≈ 0, a1 ≈ 1, a2 > 0 and a3 < 0.

Figure 6.7: Graphical representation of the deviation of the measured curve from thetheoretical curve [52]

Practically, the measurement of the different water thicknesses is performed by adding afixed amount of water in a plastic vial. First, a measurement of the empty vial was madeto be able to correct the other measurements for the attenuation of the bottom of thevial. Next, 10 ml of water was put into the vial and a measurement was performed. Thisstep was then repeated by adding each time 5 ml of water until there was 50 ml in thecup. This volume of water corresponded with a height of approximately 30 mm, equallingthe average path length crossed in a mouse.

6.2.4 In Vivo and Ex Vivo Experiments

To validate the results of the phantoms, some in vivo and ex vivo mouse tests were per-formed. These animal tests are approved by the Ghent University Ethical committee(ECD 12/24). A total of five mice were scanned. Four of these mice were injected with0.010 ml/g Fenestra VC-131 R©, while one mouse was injected with 0.015 ml/g contrastagent. The protocol used for the experiment is given in the next paragraph.


First, the animal is anaesthetised using isoflurane. Then, the contrast agent is adminis-tered and the mouse is positioned prone on the mouse bed and in the scanner. This bedis heated to avoid cooling down of the animal during the scans. Fifteen minutes after thecontrast administration, the scans can be initiated. First the animal is scanned at 90 kVp- 200 ms, then at 80 kVp - 200 ms and finally at 50 kVp - 500 ms. Once these scans arefinished, the animal is brought out of the scanner and euthanised with an overdose ofpentobarbital. Then the animal is brought back into the scanner and scans at the samesettings are performed. When the ex vivo scans are also finished the animal is frozen foruse in other contrast-enhanced ex vivo studies. Some of these steps are represented infigure 6.8.

(a) Sedation of the animal (b) Injection of the contrast agent

(c) Positioning of the mouseon the mouse bed

Figure 6.8: Overview of some of the different steps in the scan protocol

6.2.5 Material Decomposition

The material decomposition will be carried out based on some of the algorithms describedin section 4.2 and the calibration data obtained from the phantom. The focus will be onthe two material decomposition algorithm, the cone beam three material decompositionalgorithm and the empirical dual energy calibration (EDEC) algorithm described earlier.


Based on these algorithms, a MATLAB R© code was written and evaluated using the iodinequantification phantom. Once the results are sufficient using the phantom, the code willalso be tested on the mouse data obtained from the in vivo and ex vivo scans.

As the algorithms for EDEC were very time consuming and did not immediately leadto the expected results, some simulations were performed to validate the correctness ofthe algorithm and feasibility of the technique under the applied conditions.

Therefore a virtual phantom was created using ImageJ (National Institute of Health)as shown in figure 6.9(a). This phantom was based on the quarter phantom describedin the article of Stenner et al. [23]. The image consists of 1024 x 1024 pixels of 30µmand the phantom itself has a diameter of 25 mm. To the background region a densityof 0.001205 g/cm3, corresponding to air, was assigned. To the grey area, a density of1 g/cm3 corresponding to water was assigned. For the white area, two situations wereconsidered. In a first case, we assigned cortical bone with a density of 1.990 g/cm3 to thatarea. This case was used to test the algorithm as there is a large difference in density,and thus linear attenuation coefficient, for the two materials. In the second case, we as-signed an iodine mixture with a density of 1.0048 g/cm3. This density corresponds to aniodine concentration of 6 mgI/ml which is in agreement with the concentration that willbe found in the blood of the mice during the in vivo and ex vivo scans. This case wasused to validate if material separation would still work in case of very low differences inattenuation, as would be the case in the in vivo and ex vivo measurements. Once thisphantom is constructed, MC-GPU will sample the materials using Monte-Carlo methods[53]. The spectra of the 50 kVp and 90 kVp beams used here, correspond to the spectragenerated by the X-ray tube of the Triumph scanner.

(a) Virtual phantom used forthe simulations

(b) Moby phantom

Figure 6.9: Phantoms used for the EDEC simulations

Afterwards we can reconstruct the images using SIRT, an iterative reconstruction al-gorithm. 100 iterations were used for each image. The material decomposition usingEDEC is then implemented according to the algorithm described in 4.2.2. Afterwards,the material decomposition was also applied to the Moby phantom shown in figure 6.9(b).Compared to the scans taken from the phantom, these simulated images will be noiseless.

Both for the phantom measurements as for the simulations, template images and weightimages are needed to determine the calibration coefficients. The template images andweight images used for the phantom measurements are shown in figure 6.10.


(a) Template image for water (b) Template image for iodine (c) Weight image

Figure 6.10: Template images and weight images needed to find the coefficients c

6.2.6 Applications

As a final step, the possibilities of weighted images and virtual non-contrast images willbe investigated.

First, the weighting factors that optimise the mixed image for noise and contrast willbe calculated using the formulas given in section 4.2.1. Using these factors, two mixedimages can then be created and noise and CNR can be calculated for both of the images.This algorithm will be applied both on the phantom as on the data obtained from mouse2. In the phantom we will first optimise for water as background and the lowest iodineconcentration as signal region, followed by an optimisation for water as background andmuscle as signal region. In the mouse data we will optimise for muscle as background andiodine as signal.

Using these mixed images and the information on the basis materials obtained with thethree material decomposition algorithm, virtual non-contrast images can be generated byremoving the iodine content in the images. Furthermore, the iodine content can also berepresented as a colour overlay on the mixed images to represent the location of the iodinemore clearly.

Chapter 7

Results

7.1 Optimal energy combination

The results are shown in figure 7.1. The following combinations are compared: 40 - 60,40 - 70, 40 - 80, 40 - 90, 50 - 70, 50 - 80, 50 - 90, 60 - 80 and 60 - 90 kVp. The red line connectsthe data points for air, fat, water, muscle and bone, while the green line connects the datapoints for the different iodine concentrations. The shaded areas denote the 2σ confidenceinterval, or a 95% interval.

Looking at the 60 - 80 and 60 - 90 combinations, we see that both regions nearly overlap.Since this would result in more difficulties to distinguish bone from iodine, we discardthese energy combinations. Next, the combinations with 40 kVp can be considered. Al-though nearly no overlap is found between both curves, it can be seen that the data pointsseem to be deviating from the linear fit. Therefore, also these combinations are excluded.Finally, also the 50 - 70 combination is eliminated because, also for this pair, both curveshave quite much overlap.

From this experiment, it can therefore be concluded that the 50 - 80 kVp and 50 - 90 kVpenergy combinations seem to be the most optimal. In the following we will thereforecontinue to work with these energies.

7.2 Ideal scan parameters

7.2.1 Exposure time

The results are shown in figure 7.3. As mentioned in the previous section, we only con-sider the energies 50, 80 and 90 kVp.

In figure 7.3(a), the signal to noise ratio is compared for different exposure times. Itcan be seen that the SNR is constant for 80 and 90 kVp. This result was expected sincethe automatic adaptation of the current results in a constant mAs-value, which in turn

38

CHAPTER 7. RESULTS 39

Figure 7.1: Comparison of the different energy combinations. Green is iodine, red is air, fat,water, muscle and bone.


results in a constant noise value and thus a constant SNR. However, for 50 kVp the max-imal tube current is obtained for each exposure time, therefore resulting in an increasingnoise value with decreasing exposure time.

In figure 7.2, the influence of the voxel size on the spatial resolution is shown. Whenlooking at the line spread functions, it can be seen that a wire of 10µm is representedas a Gaussian function with a FWHM equal to 0.24 mm when the voxel size is 0.1 mm.Similarly, for voxel sizes of 0.05 mm and 0.02 mm, FWHM of 0.17 mm and 0.13 mm weremeasured. From these measurements, it can thus be concluded that the spatial resolutiondepends on the reconstruction voxel size. Especially for the reconstruction with voxel size0.1 mm, the spatial resolution will be determined by the voxel size itself and not by theresolution of the detector itself. It is therefore necessary to reconstruct the wire phantomwith sufficiently small voxel size, to obtain the actual resolution of the detector. For thephantom used here, 0.02 mm is the smallest voxel size achievable, as for even smaller sizes,the wire is found on the very edge of the reconstruction and determination of the spa-tial resolution may not be accurate anymore. The influence of the number of projectionangles on the spatial resolution is shown in figure 7.3(b). It can be seen that the spatialresolution remains more or less constant when varying the exposure time.

(a) Line spread function (b) Spatial resolution

Figure 7.2: Comparison of the spatial resolution for different voxel dimensions. Zoom1 =0.1 mm, zoom2 = 0.05 mm, zoom3 = 0.02 mm

In figures 7.3(c), 7.3(d), 7.3(e) and 7.3(f), the amount of contrast and the contrast tonoise ratio are represented. In figures 7.3(c) and 7.3(e), a validation of the scans is given.It can be seen that both in the case of the 40 HU and the 80 HU contrast regions, therequired values are approached. The CNR follows the same course as the SNR, becausethe CNR similarly depends on the mAs-value as the SNR does.

Based on these results, it was decided to keep the noise level constant for both the highand the low energy scans. This means that an exposure time of 200 ms for the 80 kVpand the 90 kVp scans seems to be the most ideal. For a voltage of 50 kVp, the SNRand CNR for the 80 HU region are equal to those of the other voltage settings at 400 ms,while the CNR for the 40 HU is equal at an exposure time of 500 ms. To preserve a highCNR, even for the 40 HU region, it was decided to use an exposure time of 500 ms. If this


(a) Signal to noise ratio (b) Spatial resolution

(c) Contrast difference: validation scans for the80 HU low contrast region

(d) Contrast to noise ratio for the 80 HU low con-trast region

(e) Contrast difference: validation scans for the40 HU low contrast region

(f) Contrast to noise ratio for the 40 HU low con-trast region

Figure 7.3: Image quality parameters as a function of exposure time


exposure time would lead to a too high radiation dose to the animal, it could be loweredto 400 ms. These settings will ensure the images are acquired at the most optimal quality,while keeping the dose to the animals as low as possible. The choice of these settings hasno influence on the spatial resolution as was discussed earlier.

7.2.2 Projection angles

The results for an exposure time of 500 ms are shown in figure 7.4. Similar results forexposure times of 400 ms, 300 ms and 200 ms are given in appendix A. It can be seen inthese figures that the SNR and CNR decrease when the number of projection angles isreduced. Taking a closer look at the graphs, it can be seen that the SNR decreases toapproximately three quarters of the original value when the number of projection anglesare decreased from 1024 to 512. Similarly, the CNR is approximately halved. The spatialresolution on the other hand, seems to remain nearly constant when varying the numberof projection angles. To ensure adequate image quality, it is therefore best to keep thenumber of projection angles at 1024. Performing both scans at this setting will lead toacceptable dose levels, allowing longitudinal studies.


(c) Contrast to noise ratio for the 80 HU lowcontrast region

(d) Contrast to noise ratio for the 40 HU lowcontrast region

Figure 7.4: Image quality parameters as a function of the number of projection angles for anexposure time of 500 ms


7.2.3 Energy spectrum

In this results section, an overview of different possible spectra for DECT is given. Thestandard spectra at 50 and 90 kVp and at 50 and 80 kVp obtained with a filtration of1 mm beryllium and 2 mm aluminium are shown in figure 7.5. In figure 7.6, an overviewof the mass attenuation coefficients of materials found in the body is given, togetherwith the mass attenuation coefficients of iodine and an iodine-water mixture of 6 mgI/ml.These mass attenuation coefficients were calculated using XCOM [7]. The concentrationof the iodine mixture was based on the concentration of iodine that will be found in theblood of the mice during tests performed further on in this work. It is apparent thatthe mass attenuation coefficient and the height of the K-edge peak decrease significantlyfor this low iodine concentration. For this standard spectrum, it can be seen that forthe 50 - 90 kVp combination there is only a very small difference in attenuation for thehigh and low energy spectrum. For the 50 - 80 kVp combination there is even nearly nodifference. Therefore, it may be necessary to increase the amount of separation betweenboth spectra by adding some filtration. The aim of the filtration is to increase the averageenergy of both spectra so the average energy of the 50 kVp spectrum is found just abovethe K-edge peak. From the available filtration materials, it can be seen that aluminium orberyllium are most suited for the purpose needed here, as they have a high attenuation atlow energies and a low attenuation at high energies. The most ideal filtration will cut offthe low energy X-rays without decreasing the photon count too much. Using SpekCalc,it was found that increasing the amount of aluminium filtration to 3.5 mm leads to themost acceptable results. Using this filtration, the average energy of the 50 kVp spectrumis shifted from 31.3 keV to 33.6 keV. The affixing of this filtration however also leads to adecrease in the photon count, which may result in a higher amount of noise in the images.For the 50 kVp spectrum, the maximum photon count decreases to 70 % of its originalvalue, similarly the photon count of the 90 kVp spectrum decreases to 60 % of its originalvalue and that of the 80 kVp also decreases to 70%. The resulting new spectra and theposition of their average energy with respect to the K-edge peak, can be seen in figure 7.7and 7.8 respectively. It can thus be expected that, by adding some additional aluminiumfiltration, the differences in mass attenuation coefficient for the different spectra willincrease resulting in a better material decomposition.

(a) Standard spectra for 50 and 90 kVp generatedby the Triumph

(b) Standard spectra for 50 and 80 kVp generatedby the Triumph

Figure 7.5: Spectra generated with standard filtration


Figure 7.6: Overview of the mass attenuation coefficients of different relevant materials, thevertical lines denote the average energies of the standard 50, 80 and 90 kVp spectra

(a) Spectra for 50 and 90 kVp (b) Spectra for 50 and 80 kVp

Figure 7.7: Spectra generated with an aluminium filtration of 3.5 mm

Figure 7.8: Overview of the mass attenuation coefficients of different relevant materials, thevertical lines denote the average energies of the standard 50, 80 and 90 kVp spectra with

additional aluminium filtration


7.3 Beam Hardening Correction

The results of the beam hardening correction measurements can be seen in figure 7.9(a).In this figure the log attenuation values µd are plotted versus the water thicknesses, to-gether with a third order fit to these data. It can be seen that the measurements hardlydeviate from a straight line. Furthermore, the coefficients obtained from the fit given intable 7.1, deviate from the conditions given in section 6.2.3.

In order to ascertain beam hardening is actually present, a graph of the attenuationcoefficient versus the water thickness was also made (figure 7.9(b)). The value of theattenuation coefficient µ was obtained by dividing the log attenuation value by the re-spective water thickness. In this graph, only a minor decrease of the attenuation coefficientis observed. It can therefore be concluded that nearly no beam hardening is present forthese thicknesses and thus no correction needs to be carried out.

Coefficient 50 kVp 80 kVp 90 kVpa0 0.0012 -0.0044 -0.0011a1 0.4578 0.3625 0.3267a2 -0.0356 -0.0346 0.0240a3 0.0044 0.0048 0.0033

Table 7.1: Coefficients needed for the beam hardening correction

(a) Fit of a third order polynomial to the measure-ment data

(b) Attenuation coefficient as a function of waterthickness

Figure 7.9: Results of the beam hardening correction experiment

7.4 In vivo and ex vivo experiments

As the scans were performed without additional filtration and it was shown in section7.2.3 that there is nearly no difference in mass attenuation coefficient of iodine for the50 - 80 kVp energy combination, only the results for the 50 - 90 kVp combination will bediscussed here.


The results of the in vivo experiments are shown in figure 7.10. Although five micewere scanned, the figure only shows four slices. This is because mouse 3 died before theonset of the low energy scan and thus no in vivo low energy scan could be acquired. Themeasurements for mouse 1 and 2 were performed without any problems. The acquisitionof mouse 4 has many artefacts due to the very fast and irregular breathing pattern of themouse. In the scans of mouse 5, it can be seen that the heart is enlarged compared to theother mice. This is due to the higher amount of contrast agent administered. Similarly,the results of the ex vivo tests are shown in figure 7.11.

Figure 7.10: Results of the low and high energy scans for the in vivo tests

Figure 7.11: Results of the low and high energy scans for the ex vivo tests

7.5 Material Decomposition

In the next sections, the different material decomposition algorithms applied to the datasets are discussed. Each algorithm will first be validated on a phantom. Next, thealgorithms are applied to in vivo and ex vivo data.


7.5.1 Two Material Decomposition

Phantom

The results of the two material decomposition applied to the phantom can be seen infigure 7.12. The bone and iodine image are obtained by locating the pixels in the boneregion or the iodine region of the bisector, while the soft tissue image is determined asthe remainder.

It is clear from the figure that the decomposition is quite accurate. Looking at thebone selective image, it can be seen that apart from the bone, there is a very small io-dine contribution and the glass vials containing iodine are also detected as bone. Thisis however not considered problematic, as under normal circumstances no glass will bepresent in the animal’s body. Furthermore, when looking at the iodine selective image, itcan be seen that the different iodine concentrations are correctly detected. In both theiodine selective image and the bone selective image a contribution in the soft tissue regionbetween bone and the highest iodine concentration can be detected. This is caused bystreak artefacts due to the high iodine concentration.

Figure 7.12: Validation of the two material decomposition algorithm on the phantom.a. Original image b. Bone image c. Iodine image d. Soft tissue image

In vivo experiments

The results of the two material decomposition algorithm applied to the in vivo scans areshown in figure 7.13 for an iodine injection of 0.01 ml/g and figure 7.14 for an iodineinjection of 0.015 ml/g. The results for the other mice can be found in appendix B, theremarks given below are also valid for these images.

For the lowest iodine concentration, it can be seen that the bone image again contains asmall iodine contribution. On the iodine selective image both ventricles are clearly visible,


but again a bone contribution is present.

Compared to the iodine image of mouse 2, the one of mouse 5 contains a much largercontribution of iodine. As the iodine concentration is 50% higher, this result was to beexpected. However, it is clear that apart from a higher iodine contribution, also the bonecontribution in the iodine image seems to have increased. Similarly, the bone image ofmouse 5 has a higher iodine contribution compared to that of mouse 2. The use of ahigher iodine concentration thus seems to make a correct material decomposition moredifficult.

Figure 7.13: Two material decomposition algorithm applied to the in vivo data obtained withmouse 2 a. Original image b. Bone image c. Iodine image d. Soft tissue image

Figure 7.14: Two material decomposition algorithm applied to the in vivo data obtained withmouse 5 a. Original image b. Bone image c. Iodine image d. Soft tissue image


Ex vivo experiments

Figures 7.15 and 7.16 show the results of the material decomposition applied to the exvivo scans of mouse 2 and mouse 5 respectively. The results for the other mice can befound in appendix B. Similar results were found for these mice.

Looking at the iodine image of mouse 2, it can be seen that the iodine contributionis very low. It is therefore no longer possible to distinguish both cardiac ventricles. Theseresults are in contradiction to expectations. As movement artefacts due to breathing andthe beating of the heart are removed in the ex vivo scans, it was expected that the resultswould be somewhat better than the in vivo results. However, the opposite is shown here.A possible explanation may be the relaxation of the muscles followed by stiffening afterthe euthanasia. This can result in movement of the organs and the bones in the lowenergy scan compared to the high energy scan, thus making material separation moredifficult.

The ex vivo results of mouse 5 seem to be better than those of mouse 2. The iodineimage shows a larger iodine contribution and even the cardiac ventricles can still be dis-tinguished. This higher amount of iodine is however again accompanied by a higher bonecontribution. Similarly, the iodine contribution in the bone image is also increased.

Figure 7.15: Two material decomposition algorithm applied to the ex vivo data obtained withmouse 2 a. Original image b. Bone image c. Iodine image d. Soft tissue image


Figure 7.16: Two material decomposition algorithm applied to the ex vivo data obtained withmouse 5 a. Original image b. Bone image c. Iodine image d. Soft tissue image

7.5.2 Three Material Decomposition

Compared to two material decomposition, three material decomposition yields more quan-titative results as it represents the contribution of the three basis materials in each voxel.Using these results, it is then possible to construct virtual non-contrast images with aniodine overlay.

It was decided to use the values of bone, muscle and iodine obtained from the density ca-libration phantom as attenuation coefficients in the calculations. For the value of iodine,it was possible to choose between the eight different concentrations. For the applicationof the algorithm on the phantom, it was obvious to use the highest iodine concentrationas it can be considered as pure iodine. For the mouse data, the same value was used, asusing lower iodine concentrations leads to a higher soft tissue contribution in the iodineimages, thus hampering correct material separation. Similar problems were found whenobtaining the value for iodine from the in vivo images themselves. As the concentrationof iodine in the blood is much lower than the concentration of pure iodine, the valuesin the iodine contribution images are very low and a suitable range for the grey valuesshould be chosen. The ranges used in the following images are given in table 7.2.

Basis material Phantom Mouse dataBone 0 - 1 0 - 0.3Iodine 0 - 1 0 - 0.1

Soft tissue 0 - 1 0.8 - 1

Table 7.2: Ranges of the grey values used in the images obtained with the three materialdecomposition algorithm


Phantom

The results of the three material decomposition algorithm applied to the phantom can beseen in figure 7.17. Taking into account that the colour code in the figures now representsvolume fractions, it can be seen that similar results as with the two material algorithmare obtained. The bone image again contains contributions from both bone and theglass vials, together with a very small iodine contribution. In the iodine image, it isagain clear that all iodine concentrations are detected correctly. Furthermore, each of theconcentrations has a different shade of grey assigned to it. Three material decompositionthus allows also to detect different concentrations of a material. Again, there is also asmall contribution of bone to the iodine image. In the soft tissue image, it can be seenthat nearly all soft tissue regions have a grey value of approximately one. The differencewith the two material decomposition algorithm is that there is now also a soft tissuecontribution detected in the iodine vials corresponding to the different concentrationsused.

Figure 7.17: Validation of the three material decomposition algorithm on the phantom.a. Original image b. Bone image (f1) c. Iodine image (f2) d. Soft tissue image (f3)

In vivo experiments

Applying the three material decomposition algorithm on the in vivo data from mouse 2and 5 leads to figures 7.18 and 7.19. Again, the results for the other mice can be foundin appendix B and the analysis given here is also valid for those scans.

In general, the same conclusions can be made for the three material decomposition al-gorithms as were made for the two material decomposition algorithm before. However,when taking a closer look at the iodine image of mouse 2, it can be seen that, besides bothcardiac ventricles, also the aorta is clearly visible, together with part of the pulmonaryveins. The soft tissue image clearly represents the blood contribution in the heart.

In figure 7.19, the iodine contribution is again much higher, but at the same time the


bone-iodine decomposition is worse. Furthermore, there is a higher contribution of softtissue in the bone and iodine images.

Figure 7.18: Three material decomposition algorithm applied to the in vivo data obtained withmouse 2 a. Original image b. Bone image (f1) c. Iodine image (f2) d. Soft tissue image (f3)

Figure 7.19: Three material decomposition algorithm applied to the in vivo data obtained withmouse 5 a. Original image b. Bone image (f1) c. Iodine image (f2) d. Soft tissue image (f3)

Ex vivo experiments

The results of the three material decomposition algorithm applied to the ex vivo data ofmouse 2 and 5 can be found in figures 7.20 and 7.21. The results for the other mice canbe found in appendix B.

Similarly as for the two material decomposition algorithm, the three material decompo-sition algorithm seems to fail when applied to the ex vivo data of mouse 2. Applying thealgorithm on the data of mouse 5 allows to detect iodine in the cardiac ventricles, but atthe same time the bone-iodine decomposition is worse.


Figure 7.20: Three material decomposition algorithm applied to the ex vivo data obtained withmouse 2 a. Original image b. Bone image (f1) c. Iodine image (f2) d. Soft tissue image (f3)

Figure 7.21: Three material decomposition algorithm applied to the ex vivo data obtained withmouse 5 a. Original image b. Bone image (f1) c. Iodine image (f2) d. Soft tissue image (f3)

7.5.3 EDEC

Phantom measurements

The low and high energy image acquired from the phantom are shown in figure 7.22. Afirst step in the algorithm is to construct 25 basis images based on these high and lowenergy images. An overview of these 25 images is given in figure 7.23.

Once these 25 basis images are constructed, the coefficients c1 and c2 for material decom-position need to be determined using a template image and a weight image. Using thesecoefficients, the material selective image can then be made using a linear combination ofthe obtained coefficients and the raw data of the basis images. The results are shown in


figure 7.24. These results however do not fulfil the expectations. Even though part of thewater region seems to be removed in the iodine image, both materials are not separatedcompletely. Some possible causes may be, an error in the algorithm, inadequate separa-tion of the high and low energy spectra or too low iodine concentration. To rule out someof these causes, several simulations were performed.

(a) Low energy image (b) High energy image

Figure 7.22: Low and high energy scans of the EDEC calibration phantom

Figure 7.23: Overview of the different basis images obtained from the phantom


(a) Water image (b) Iodine image

Figure 7.24: Material selective images

Simulations

As mentioned before, the first simulations are performed using a calibration phantomcontaining water and cortical bone as basis materials to validate the used algorithm. Anoverview of the 25 basis images is given in figure 7.25. Similar template and weight imagesas for the phantom measurements are then made and material separation is performed.The result of this material separation can be seen in figure 7.26, together with the highand low energy image. In comparison to the results of the phantom experiments, thesimulations do lead to a good material separation. In the bone image, there is no contri-bution of the water, while in the water image there is a small contribution of bone in theedges of the phantom.

Figure 7.25: Overview of the basis images used for the simulations based on the water - bonecalibration phantom


(a) High energy image (b) Low energy image

(c) Water image (d) Bone image

Figure 7.26: Results of the simulation using the water - bone calibration phantom



Figure 7.27: Results of the simulation on the Moby phantom using the water - bone calibrationcoefficients


Based on these results, it was decided to apply the calibration coefficients on the Mobymouse phantom to see if similar results could be achieved. The high and low energyimage together with the results of the material separation can be seen in figure 7.27.As expected, a good material decomposition was carried out. Muscle, fat and blood arecorrectly distinguished as a water-like material, while the bone image only contains con-tributions from bone.

Using these results, it can be concluded that the implementation of this algorithm isable to correctly execute the material decomposition. A second step is then to adaptthe materials in the phantom so they better meet the real situation as presented in thephantom measurements. A similar method as for the first simulation was applied andleads to the results shown in figure 7.28. Compared to the results of the first simulation,these results look far worse. Some separation of the materials can be seen, but bothmaterial selective images contain contributions of the other basis material. Similar to theprevious simulation, the calibration coefficients were also applied to the Moby phantom.As this phantom normally does not contain an iodine contribution, the blood in the heartand the aorta was replaced by the iodine mixture. When looking at the results given infigure 7.29, it can be seen that the material separation completely fails. Soft tissue is notrecognised as water and iodine is seen as cortical bone instead of the iodine mixture. Thismay be due to inadequate separation of both spectra or a too small difference in densitybetween both materials.



Figure 7.28: Results of the simulation using the water - iodine calibration phantom




Figure 7.29: Results of the simulation on the Moby phantom using the water - iodinecalibration coefficients

7.6 Applications

7.6.1 Weighted images

The formulas were evaluated using the data of both the phantom and mouse 2. In thephantom, we first optimised for noise and contrast between the water region (background)and the region containing the lowest iodine concentration (signal). The results are givenin tables 7.3, 7.4 and figure 7.30. Table 7.3 contains the weighting factors that can beused to create mixed images with minimal noise or maximal contrast. Table 7.4 containsthe values for noise and CNR calculated on the original images and the weighted imagesand figure 7.30 contains the corresponding images.

It can be seen in table 7.3 that the weighting factors used to optimise for noise andcontrast are nearly equal. This can be explained by the way the image protocols wereselected. As we have chosen to keep the amount of noise and contrast in the imagesconstant for that single energy, the weighting factors used to optimise for contrast andnoise are also expected to be nearly equal. Because of this property, no differences can beobserved between the noise optimised image and the contrast optimised image in figure7.30. Furthermore, it can be seen in table 7.4, that the low energy image seems to havebetter noise properties than the high energy image, while we expect the contrast to bebetter in the low energy images but the noise to be worse. This observation can again


be explained by the choice of the imaging parameters. As it was chosen to keep thecontrast to noise ratio constant, even for very low contrast regions, the signal to noiseratio and the contrast to noise ratio for the other low contrast region are somewhat higherfor the low energy image than for the higher energy image, thus explaining the results.Additionally, it can also be seen that the measured noise in the noise optimised imageis somewhat higher than the calculated minimal value. Similarly, the measured contrastto noise ratio for the contrast optimised image is also found to be somewhat lower thanthe calculated maximum value. This may be due to fluctuations in the images that maylead to differences in the grey values causing the standard deviation to deviate from theminimal value calculated. Finally, when taking a closer look at the weighted images infigure 7.30, the effect of decreased noise can be clearly seen, but the increase in contrastis not visible on these images. However, as discussed before, the increase in contrast canbe measured.

wL(σmin) 0.561σmin 110.072

wL(CNRmax) 0.559CNRmax 11.917

Table 7.3: Weighting factors and optimised values for noise and CNR using water and iodinefor optimisation

LE image HE image Noise optimised Contrast optimisedσ 136.056 131.650 120.523 120.531

CNR 8.817 6.888 10.199 10.196

Table 7.4: Measured values for the noise and CNR using water and iodine for optimisation

A similar discussion can be given when the phantom images are optimised using wateras background material and muscle as signal material. The results are given in tables7.3, 7.4 and figure 7.30. Compared to the previous case, the weighting factor used tooptimise for contrast here is much higher than the weighting factor for noise. This can beexplained by looking at the attenuation values for water, muscle and iodine. Compared toiodine, the attenuation coefficient of muscle will be much closer to the attenuation valueof water. Therefore, the initial contrast difference between water and muscle will be muchlower, thus requiring more optimisation. In figure 7.31, a clear difference in the amountof noise for both optimised images can be observed, corresponding to the measurements.The increase in contrast is again more difficult to observe.

wL(σmin) 0.571σmin 109.676


Table 7.5: Weighting factors and optimised values for noise and CNR using water and musclefor optimisation

Finally, the algorithm was also applied to the in vivo data of mouse 2. Here, the optimi-sation was performed for muscle in the cardiac region and iodine. A similar discussion ofthe results given in tables 7.7, 7.8 and figure 7.32 can be performed. It can be noticed here



(c) Noise optimised image (d) Contrast optimised image

Figure 7.30: Comparison of the original high and low energy images with the weighted imagesusing water and iodine for optimisation


CNR 0.572 0.079 0.350 0.539

Table 7.6: Measured values for the noise and CNR using water and muscle for optimisation

that the measured values are found to be much closer to the calculated values during theoptimisation. As was the case for the phantom, no differences can be observed betweenthe noise and the contrast optimised images in figure 7.32. This is also reflected in themeasured values.

wL(σmin) 0.581σmin 102.294


Table 7.7: Weighting factors and optimised values for noise and CNR using muscle and iodinefor optimisation

7.6.2 Virtual non-contrast

Another application, is the generation of virtual non-contrast images. The feasibility ofthis technique is tested using the phantom and the in vivo data of mouse 2. The gen-




Figure 7.31: Comparison of the original high and low energy images with the weighted imagesusing water and muscle for optimisation


CNR 1.789 1.187 2.134 2.149

Table 7.8: Measured values for the noise and CNR using muscle and iodine for optimisation

eration of these images is based on the usage of an iodine contribution image as wasacquired using the three material decomposition algorithm. This image can then be usedto subtract the iodine contribution from the original image. Furthermore, this image canalso be used to create an iodine overlay on the original image.

The results for the phantom can be seen in figure 7.33. The subtraction of the iodinecontribution is especially apparent for the highest iodine concentration. There it can beseen that the entire contribution is removed and only the glass tube remains. The colour-coded iodine overlay, shows the location of the iodine contributions with respect of theoriginal image, and the intensity of the red shade is a measure for the amount of iodinepresent.




Figure 7.32: Comparison of the original high and low energy images with the weighted imagesusing muscle and iodine for optimisation

(a) Mixed image (b) Iodine contribution

(c) Virtual non-contrast image (d) Image with a colour-coded iodineoverlay

Figure 7.33: Results of the virtual non-contrast calculation on the phantom


Similarly, the results of the mouse data are shown in figure 7.34. Compared to the phan-tom, the intensities in the iodine image are much lower, resulting in nearly no subtractionof the iodine content. In order to assess whether using a lower iodine concentration asbasis material in the three material algorithm has an influence on the iodine subtraction,a second virtual non-contrast image was generated. In this figure, the subtraction ofiodine is more apparent in the heart region, but at the same time some of the bone isalso subtracted. Furthermore, when looking at the iodine colour-overlay image, it can beseen that a lot of muscle tissue has been detected as iodine. These two effects are dueto inadequate spectral separation between the high and low energy images and therefore,the effect of better spectral separation should be investigated.

(a) Mixed image (b) Iodine contribution

(c) Virtual non-contrast image (d) Image with a colour-coded iodineoverlay

(e) Iodine contribution calculatedusing the lowest iodine concentrationin the phantom as basis material forthe iodine attenuation coefficient

(f) Virtual non-contrast image gen-erated using the second iodine image

Figure 7.34: Results of the virtual non-contrast calculations on the mouse data

Chapter 8

Conclusion

In this chapter, an overview of the main results of this thesis will be given.

8.1 Dual energy scan protocol

Based on the results of the conducted experiments, the following protocol is considered asthe most ideal for dual energy applications on the standard FLEX Triumph-II microCTwithout addition of extra filtration layers.

• First, the animal is sedated using isoflurane.

• Next, 0.010 ml/g Fenestra VC-131 R© contrast agent is administered to the animals.

• Then, the mouse is placed prone on the animal bed and positioned into the scanner.The animal bed is heated to avoid cooling down of the animal during the scans.

• After waiting at least 15 minutes, the mouse is scanned at the following settings:

– fly mode

– high energy scan: 90 kVp, 200 ms, 480µA

– low energy scan: 50 kVp, 500 ms, 790µA

– 1024 projections

– magnification 2

– 1 x 1 binning

• Afterwards the images are reconstructed with a voxel size of 100µm.

Even though no dose measurement were performed during this thesis, it is expected thatthe radiation dose will be acceptable for the animals. If future dose measurement howeverwould demonstrate that the radiation dose is too high, reducing the exposure time for thelow energy scans to 400 ms would be acceptable.

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CHAPTER 8. CONCLUSION 65

As the animals were euthanised immediately after termination of the in vivo scans, noinformation on the longitudinal effects on the animals are available. However, as the pro-tocol executed here is quite similar to a standard contrast exam, no significant problemsare expected. Furthermore, of the five mice scanned, only one mouse died during the invivo scans.

8.2 Material decomposition

Three different material decomposition algorithms have been discussed here. Althoughthe two and three material decomposition algorithms led to promising results, the de-composition using empirical dual energy calibration completely failed. A possible reasonmay be the lack of separation between the high and low energy spectrum used. As thismay also be the reason for the inadequate separation of bone and iodine in the in vivostudies, further research on the spectra should be performed. It was already shown thatan additional aluminium filtration of 1.5 mm may result in an improved spectral separa-tion. However, the addition of extra filtration also results in a decrease in photon count.Therefore, a careful analysis of the scan parameters should be performed once again to en-sure the image quality is still adequate and moreover the amount of noise remains withinlimits.

8.3 Applications

In this work, we have focussed on two major applications of DECT. While the results ofthe weighted images seemed adequate, the generation of virtual non-contrast images wasnot optimal when using the in vivo data. This is due to the very low intensities of iodineobtained using the three material decomposition algorithm. Similarly as for the materialdecomposition, here it can also be concluded that more extensive research to increase thespectral separation may be required.

8.4 General conclusion

In this master dissertation, the feasibility of dual energy microCT on a standard FLEXTriumph-II system was shown using phantom and in vivo and ex vivo validation. Aftersearching the ideal dual energy imaging protocol for microCT, some material decomposi-tion algorithms were applied. The results obtained here are promising, but the materialdecomposition is not yet perfect. Therefore, more extensive research on the spectralseparation may be needed to obtain even better results.

Appendix A

Ideal scan parameters: Other results


(c) Contrast to noise ratio for the 80 HU low con-trast region


Figure A.1: Image quality parameters as a function of the number of projection angles for anexposure time of 400 ms

66

APPENDIX A. IDEAL SCAN PARAMETERS: OTHER RESULTS 67





APPENDIX A. IDEAL SCAN PARAMETERS: OTHER RESULTS 68





Appendix B

In vivo and ex vivo experiments

B.1 In vivo results

(a) Two material decomposition (b) Three material decomposition

Figure B.1: Material decomposition algorithms applied on the in vivo data obtained withmouse 1 a. Original image b. Bone image c. Iodine image d. Soft tissue image

69

APPENDIX B. IN VIVO AND EX VIVO EXPERIMENTS 70


Figure B.2: Material decomposition algorithms applied on the in vivo data obtained withmouse 4 a. Original image b. Bone image c. Iodine image d. Soft tissue image

B.2 Ex vivo results


Figure B.3: Material decomposition algorithms applied on the ex vivo data obtained withmouse 1 a. Original image b. Bone image c. Iodine image d. Soft tissue image

APPENDIX B. IN VIVO AND EX VIVO EXPERIMENTS 71





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