Computed Tomography (CT) is a medical imaging modality which acquires anatomical data via the unique x-ray attenuation of materials. Yet, some clinically important materials remain difficult to distinguish with current CT technology. Spectral CT is an emerging technology which acquires multiple CT datasets for specific x-ray spectra. These spectra provide a fingerprint that allow materials to be distinguished that would otherwise look the same on conventional CT. The unique characteristics of spectral CT data motivates research into novel visualization techniques. In this thesis, we aim to provide the foundation for visualizing spectral CT data. Our initial investigation of similar multi-variate data types identified intermixing as a promising visualization technique. This promoted the development of a generic, modular and extensible intermixing framework. Therefore, the contribution of our work is a framework supporting the construction, analysis and storage of algorithms for visualizing spectral CT studies. To allow evaluation, we implemented the intermixing framework in an application called MARSCTExplorer along with a standard set of volume visualization tools. These tools provide user-interaction as well as supporting traditional visualization techniques for comparison. We evaluated our work with four spectral CT studies containing materials indistinguishable by conventional CT. Our results confirm that spectral CT can distinguish these materials, and reveal how these materials might be visualized with our intermixing framework.

During my time as a PhD student at the Physics of Medical Imaging group at KTH, I have taken part in the work of developing a photon counting spectrally resolved silicon detector for clinical computed tomography. This work has largely motivated the direction of my research, and is the main reason for my focus on certain issues. Early in the work, a need to quantify and optimize the performance of a spectrally resolved detector was identified. A large part of my work have thus consisted of reviewing conventional methods used for performance quantification and optimization in computed tomography, and identifying which are best suited for the characterization of a spectrally resolved system. In addition, my work has included comparisons of conventional systems with the detector we are developing. The collected result after a little more than four years of work are four publications and three conference papers. This compilation thesis consists of five introductory chapters and my four publications. The introductory chapters are not self-contained in the sense that the theory and results from all my published work are included. Rather, they are written with the purpose of being a context in which the papers should be read. The first two chapters treat the general purpose of the introductory chapters, and the theory of computed tomography including the distinction between conventional, non-spectral, computed tomography, and different practical implementations of spectral computed tomography. The second chapter consists of a review of the conventional methods developed for quantification and optimization of image quality in terms of detectability and signal-to-noise ratio, part of which are included in my published work. In addition, the theory on which the method of material basis decomposition is based on is presented, together with a condensed version of the results from my work on the comparison of two systems with fundamentally different practical solutions for material quantification. In the fourth chapter, previously unpublished measurements on the photon counting spectrally resolved detector we are developing are presented, and compared to Monte Carlo simulations. In the fifth and final chapter, a summary of the appended publications is included. / <p>QC 20150303</p>

Computed tomography (CT) is a widely used medical imaging modality. By rotating an x-ray tube and an x-ray detector around the patient, a CT scanner is able to measure the x-ray transmission from all directions and form an image of the patient’s interior. CT scanners in clinical use today all use energy-integrating detectors, which measure the total incident energy for each measurement interval. A photon-counting detector, on the other hand, counts the number of incoming photons and can in addition measure the energy of each photon by comparing it to a number of energy thresholds. Using photon- counting detectors in computed tomography could lead to improved signal-to-noise ratio, higher spatial resolution and improved spectral imaging which allows better visualization of contrast agents and more reliable quantitative measurements. In this Thesis, the feasibility of using a photon-counting silicon-strip detector for CT is investigated. In the first part of the Thesis, the necessary performance requirements on such a detector is investigated in two different areas: the detector element homogeneity and the capability of handling high photon fluence rates. A metric of inhomogeneity is proposed and used in a simulation study to evaluate different inhomogeneity compensation methods. Also, the photon fluence rate incident on the detector in a scanner in clinical use today is investigated for different patient sizes through dose rate measurements together with simulations of transmission through patient im- ages. In the second part, a prototype detector module is used to demonstrate new applications enabled by the energy resolution of the detector. The ability to generate material-specific images of contrast agents with iodine and gadolinium is demonstrated. Furthermore, it is shown theoretically and ex- perimentally that interfaces in the image can be visualized by imaging the so-called nonlinear partial volume effect. The results suggest that the studied silicon-strip detector is a promising candidate for photon-counting CT.

Spectral computed tomography with energy-resolving detectors has a potential to improve the detectability of images and correspondingly reduce the radiation dose to patients by extracting and properly using the energy information in the broad x-ray spectrum. A silicon photon-counting detector has been developed for spectral CT and it has successfully solved the problem of high photon flux in clinical CT applications by adopting the segmented detector structure and operating the detector in edge-on geometry. The detector was evaluated by both the simulation and measurements. The effects of energy loss and charge sharing on the energy response of this segmented silicon strip detector with different pixel sizes were investigated by Monte Carlo simulation and a comparison to pixelated CdTe detectors is presented. The validity of spherical approximations of initial charge cloud shape in silicon detectors was evaluated and a more accurate statistical model has been proposed. A photon-counting energy-resolving application specific integrated circuit (ASIC) developed for spectral CT was characterized extensively by electrical pulses, pulsed laser and real x-ray photons from both the synchrotron and an x-ray tube. It has been demonstrated that the ASIC performs as designed. A noise level of 1.09 keV RMS has been measured and a threshold dispersion of 0.89 keV RMS has been determined. The count rate performance of the ASIC in terms of count loss and energy resolution was evaluated by real x-rays and promising results have been obtained. The segmented silicon strip detector was evaluated using synchrotron radiation. An energy resolution of 16.1% has been determined with 22 keV photons in the lowest flux limit, which deteriorates to 21.5% at an input count rate of 100 Mcps mm−2. The fraction of charge shared events has been estimated and found to be 11.1% for 22 keV and 15.3% for 30 keV. A lower fraction of charge shared events and an improved energy resolution can be expected by applying a higher bias voltage to the detector. / <p>QC 20121123</p>

The goal in medical x-ray imaging is to obtain the image quality requiredfor a given detection task, while ensuring that the patient dose is kept as lowas reasonably achievable. The two most common strategies for dose reductionare: optimizing incident x-ray beams and utilizing energy informationof transmitted beams with new detector techniques (spectral imaging). Inthis thesis, dose optimization schemes were investigated in two x-ray imagingsystems: digital mammography and computed tomography (CT). In digital mammography, the usefulness of anti-scatter grids was investigatedas a function of breast thickness with varying geometries and experimentalconditions. The general conclusion is that keeping the grid is optimalfor breasts thicker than 5 cm, whereas the dose can be reduced without a gridfor thinner breasts. A photon-counting silicon-strip detector developed for spectral mammographywas characterized using synchrotron radiation. Energy resolution, ΔE/Ein, was measured to vary between 0.11-0.23 in the energy range 15-40 keV, which is better than the energy resolution of 0.12-0.35 measured inthe state-of-the-art photon-counting mammography system. Pulse pileup hasshown little effect on energy resolution. In CT, the performance of a segmented silicon-strip detector developedfor spectral CT was evaluated and a theoretical comparison was made withthe state-of-the-art CT detector for some clinically relevant imaging tasks.The results indicate that the proposed photon-counting silicon CT detector issuperior to the state-of-the-art CT detector, especially for high-contrast andhigh-resolution imaging tasks. The beam quality was optimized for the proposed photon-counting spectralCT detector in two head imaging cases: non-enhanced imaging and Kedgeimaging. For non-enhanced imaging, a 120-kVp spectrum filtered by 2half value layer (HVL) copper (Z = 29) provides the best performance. Wheniodine is used in K-edge imaging, the optimal filter is 2 HVL iodine (Z = 53)and the optimal kVps are 60-75 kVp. In the case of gadolinium imaging, theradiation dose can be minimized at 120 kVp filtered by 2 HVL thulium (Z =69). / <p>QC 20160401</p>

La tomodensitométrie par rayons-X (CT) est une modalité d’imagerie produisant une carte tridimensionnelle du coefficient d’atténuation des rayons-X d’un objet. En radiothérapie, le CT fournit de l’information anatomique et quantitative sur le patient afin de permettre la planification du traitement et le calcul de la dose de radiation à livrer. Le CT a plusieurs problèmes, notamment (1) une limitation au niveau de l’exactitude des paramètres physiques quantitatifs extraits du patient, et (2) une sensibilité aux biais causés par des artéfacts de durcissement du faisceau. Enfin, (3) dans le cas où le CT est fait en présence d’un agent de contraste pour améliorer la planification du traitement, il est nécessaire d’effectuer un deuxième CT sans agent de contraste à des fins de calcul de dose, ce qui augmente la dose au patient. Ces trois problèmes limitent l’efficacité du CT pour certaines modalités de traitement qui sont plus sensibles aux incertitudes comme la protonthérapie. Le CT spectral regroupe un ensemble de méthodes pour produire plusieurs cartes d’atténuation des rayons-X moyennées sur différentes plages énergétiques. L’information supplémentaire, pondérée en énergie qui est obtenue permet une meilleure caractérisation des matériaux analysés. Le potentiel de l’une de ces modalités spectrales, le CT bi-énergie (DECT), est déjà bien démontré en radiothérapie, alors qu’une approche en plein essor, le CT spectral à comptage de photons (SPCCT), promet davantage d’information spectrale à l’aide de détecteurs discriminateurs en énergie. Par contre, le SPCCT souffre d’un bruit plus important et d’un conditionnement réduit. Cette thèse investigue la question suivante : y a-t-il un bénéfice à utiliser plus d’information résolue en énergie, mais de qualité réduite pour la radiothérapie ? La question est étudiée dans le contexte des trois problèmes ci-haut. Tout d’abord, un estimateur maximum a posteriori (MAP) est introduit au niveau de la caractérisation des tissus post-reconstruction afin de débruiter les données du CT spectral. L’approche est validée expérimentalement sur un DECT. Le niveau de bruit du pouvoir d’arrêt des protons diminue en moyenne d’un facteur 3.2 à l’aide de l’estimateur MAP. Celui-ci permet également de conserver généralement le caractère quantitatif des paramètres physiques estimés, le pouvoir d’arrêt variant en moyenne de 0.9% par rapport à l’approche conventionnelle. Ensuite, l’estimateur MAP est adapté au contexte de l’imagerie avec agent de contraste. Les résultats numériques démontrent un bénéfice clair à utiliser le SPCCT pour l’imagerie virtuellement sans contraste par rapport au DECT, avec une réduction de l’erreur RMS sur le pouvoir d’arrêt des protons de 2.7 à 1.4%. Troisièmement, les outils développés ci-haut sont validés expérimentalement sur un micro-SPCCT de la compagnie MARS Bioimaging, dont le détecteur à comptage de photons est le Medipix 3, qui est utilisé pour le suivi de particules au CERN. De légers bénéfices au niveau de l’estimation des propriétés physiques à l’aide du SPCCT par rapport au DECT sont obtenus pour des matériaux substituts à des tissus humains. Finalement, une nouvelle paramétrisation du coefficient d’atténuation pour l’imagerie pré-reconstruction est proposée, dans le but ultime de corriger les artéfacts de durcissement du faisceau. La paramétrisation proposée élimine les biais au niveau de l’exactitude de la caractérisation des tissus humains par rapport aux paramétrisations existantes. Cependant, aucun avantage n’a été obtenu à l’aide du SPCCT par rapport au DECT, ce qui suggère qu’il est nécessaire d’incorporer l’estimation MAP dans l’imagerie pré-reconstruction via une approche de reconstruction itérative. / X-ray computed tomography (CT) is an imaging modality that produces a tridimensional map of the attenuation of X-rays by the scanned object. In radiation therapy, CT provides anatomical and quantitative information on the patient that is required for treatment planning. However, CT has some issues, notably (1) a limited accuracy in the estimation of quantitative physical parameters of the patient, and (2) a sensitivity to biases caused by beam hardening artifacts. Finally, (3) in the case where contrast-enhanced CT is performed to help treatment planning, a second scan with no contrast agent is required for dose calculation purposes, which increases the overall dose to the patient. Those 3 problems limit the efficiency of CT for some treatment modalities more sensitive to uncertainties, such as proton therapy. Spectral CT regroups a set of methods that allows the production of multiple X-ray attenuation maps evaluated over various energy windows. The additional energy-weighted information that is obtained allows better material characterization. The potential of one spectral CT modality, dual-energy CT (DECT), is already well demonstrated for radiation therapy, while an upcoming method, spectral photon counting CT (SPCCT), promises more spectral information with the help of energy discriminating detectors. Unfortunately, SPCCT suffers from increased noise and poor conditioning. This thesis thus investigates the following question: is there a benefit to using more, but lower quality energy-resolved information for radiotherapy? The question is studied in the context of the three problems discussed earlier. First, a maximum a posteriori (MAP) estimator is introduced for post-reconstruction tissue characterization for denoising purposes in spectral CT. The estimator is validated experimentally using a commercial DECT. The noise level on the proton stopping power is reduced, on average, by a factor of 3.2 with the MAP estimator. The estimator also generally con- serves the quantitative accuracy of estimated physical parameters. For instance, the stopping power varies on average by 0.9% with respect to the conventional approach. Then, the MAP estimation framework is adapted to the context of contrast-enhanced imaging. Numerical results show clear benefits when using SPCCT for virtual non-contrast imaging compared to DECT, with a reduction of the RMS error on the proton stopping power from 2.7 to 1.4%. Third, the developed tools are validated experimentally on a micro-SPCCT from MARS Bioimaging, which uses the Medipix 3 chip as a photon counting detector. Small benefits in the accuracy of physical parameters of tissue substitutes materials are obtained. Finally, a new parametrization of the attenuation coefficient for pre-reconstruction imaging is pro- posed, whose ultimate aim is to correct beam hardening artifacts. In a simulation study, the proposed parametrization eliminates all biases in the estimated physical parameters of human tissues, which is an improvement upon existing parametrizations. However, no ad- vantage has been obtained with SPCCT compared to DECT, which suggests the need to incorporate MAP estimation in the pre-reconstruction framework using an iterative reconstruction approach.

Les quantités physiques des tissus imagés par tomodensitométrie peuvent être calculées à l’aide d’algorithmes de caractérisation de tissus. Le développement de nouvelles technologies d’imagerie par tomodensitométrie spectrale a stimulé le domaine de la caractérisation de tissus à un point tel qu’il est maintenant difficile de comparer les performances des multiples algorithmes de caractérisation de tissus publiés dans les dernières années. De même, la difficulté à comparer les performances des algorithmes de caractérisation de tissus rend leur utilisation dans des projets de recherche clinique difficile. Ce projet a comme but de créer un environnement de simulation robuste et fidèle à la réalité dans lequel des techniques de caractérisation de tissus pourront être développées et comparées. De plus, la librairie de calcul servira comme tremplin pour facilement appliquer des méthodes de caractérisation de tissus dans des collaborations cliniques. En particulier, une des méthodes de caractérisation de tissus incluse dans la librairie de calcul sera appliquée sur des données cliniques pour produire des cartes de concentration d’iode dans le cadre d’un projet de recherche sur la récurrence de cancers otorhinolaryngologiques. De surcroît, deux autres techniques de caractérisation de tissus et un algorithme de correction d’artefacts de durcissement de faisceau seront implémentés dans la librairie de calcul scientifique. Conjointement, un module pour la simulation de patients virtuels sera dévelopé et intégré à la librairie de calcul. / The physical quantities of tissues imaged by computed tomography can be calculated using tissue characterization algorithms. The development of new spectral computed tomography scanners stimulated the field of tissue characterization to such an extent that it is now difficult to compare the performances of the multiple tissue characterization algorithms available in the literature. In addition, the difficulty in comparing the tissue characterization algorithms’ performances makes it difficult to include them in clinical research projects. The goal of this project is to create a robust and physically accurate simulation environment in which tissue characterization algorithms can be developed and compared. Furthermore, the scientific computing library will serve as a springboard to easily apply tissue characterization methods in clinical collaborations. In particular, one of the tissue characterization methods included in the scientific computing library will be applied on clinical data to produce iodine concentration maps for a clinical research project on head and neck cancer recurrence. Moreover, two additional tissue characterization algorithms and a technique for the correction of beam hardening artefacts will be implemented in the scientific computing library. Coincidentally, the virtual patient simulation environment will be developed.