Doctor of Philosophy

dissertationX-ray computed tomography (CT) is a widely popular medical imaging technique that allows for viewing of in vivo anatomy and physiology. In order to produce high-quality images and provide reliable treatment, CT imaging requires the precise knowledge of t

Doctor of Philosophy

dissertationThe statistical study of anatomy is one of the primary focuses of medical image analysis. It is well-established that the appropriate mathematical settings for such analyses are Riemannian manifolds and Lie group actions. Statistically defined atlases, in which a mean anatomical image is computed from a collection of static three-dimensional (3D) scans, have become commonplace. Within the past few decades, these efforts, which constitute the field of computational anatomy, have seen great success in enabling quantitative analysis. However, most of the analysis within computational anatomy has focused on collections of static images in population studies. The recent emergence of large-scale longitudinal imaging studies and four-dimensional (4D) imaging technology presents new opportunities for studying dynamic anatomical processes such as motion, growth, and degeneration. In order to make use of this new data, it is imperative that computational anatomy be extended with methods for the statistical analysis of longitudinal and dynamic medical imaging. In this dissertation, the deformable template framework is used for the development of 4D statistical shape analysis, with applications in motion analysis for individualized medicine and the study of growth and disease progression. A new method for estimating organ motion directly from raw imaging data is introduced and tested extensively. Polynomial regression, the staple of curve regression in Euclidean spaces, is extended to the setting of Riemannian manifolds. This polynomial regression framework enables rigorous statistical analysis of longitudinal imaging data. Finally, a new diffeomorphic model of irrotational shape change is presented. This new model presents striking practical advantages over standard diffeomorphic methods, while the study of this new space promises to illuminate aspects of the structure of the diffeomorphism group

PET/MRI 및 MR-IGRT를 위한 MRI 기반 합성 CT 생성의 타당성 연구

학위논문 (박사) -- 서울대학교 대학원 : 의과대학 의과학과, 2020. 8. 이재성.Over the past decade, the application of magnetic resonance imaging (MRI) in the field of diagnosis and treatment has increased. MRI provides higher soft-tissue contrast, especially in the brain, abdominal organ, and bone marrow without the expose of ionizing radiation. Hence, simultaneous positron emission tomography/MR (PET/MR) system and MR-image guided radiation therapy (MR-IGRT) system has recently been emerged and currently available for clinical study. One major issue in PET/MR system is attenuation correction from MRI scans for PET quantification and a similar need for the assignment of electron densities to MRI scans for dose calculation can be found in MR-IGRT system. Because the MR signals are related to the proton density and relaxation properties of tissue, not to electron density. To overcome this problem, the method called synthetic CT (sCT), a pseudo CT derived from MR images, has been proposed. In this thesis, studies on generating synthetic CT and investigating the feasibility of using a MR-based synthetic CT for diagnostic and radiotherapy application were presented. Firstly, MR image-based attenuation correction (MR-AC) method using level-set segmentation for brain PET/MRI was developed. To resolve conventional inaccuracy MR-AC problem, we proposed an improved ultrashort echo time MR-AC method that was based on a multiphase level-set algorithm with main magnetic field inhomogeneity correction. We also assessed the feasibility of level-set based MR-AC method, compared with CT-AC and MR-AC provided by the manufacturer of the PET/MRI scanner. Secondly, we proposed sCT generation from the low field MR images using 2D convolution neural network model for MR-IGRT system. This sCT images were compared to the deformed CT generated using the deformable registration being used in the current system. We assessed the feasibility of using sCT for radiation treatment planning from each of the patients with pelvic, thoraic and abdominal region through geometric and dosimetric evaluation.지난 10년간 진단 및 치료분야에서 자기공명영상(Magnetic resonance imaging; MRI) 의 적용이 증가하였다. MRI는 CT와 비교해 추가적인 전리방사선의 피폭없이 뇌, 복부 기관 및 골수 등에서 더 높은 연조직 대비를 제공한다. 따라서 MRI를 적용한 양전자방출단층촬영(Positron emission tomography; PET)/MR 시스템과 MR 영상 유도 방사선 치료 시스템(MR-image guided radiation therapy; MR-IGRT)이 진단 및 치료 방사선분야에 등장하여 임상에 사용되고 있다. PET/MR 시스템의 한 가지 주요 문제는 PET 정량화를 위한 MRI 스캔으로부터의 감쇠 보정이며, MR-IGRT 시스템에서 선량 계산을 위해 MR 영상에 전자 밀도를 할당하는 것과 비슷한 필요성을 찾을 수 있다. 이는 MR 신호가 전자 밀도가 아닌 조직의 양성자 밀도 및 T1, T2 이완 특성과 관련이 있기 때문이다. 이 문제를 극복하기 위해, MR 이미지로부터 유래된 가상의 CT인 합성 CT라 불리는 방법이 제안되었다. 본 학위논문에서는 합성 CT 생성 방법 및 진단 및 방사선 치료에 적용을 위한 MR 영상 기반 합성 CT 사용의 임상적 타당성을 조사하였다. 첫째로, 뇌 PET/MR를 위한 레벨셋 분할을 이용한 MR 이미지 기반 감쇠 보정 방법을 개발하였다. MR 이미지 기반 감쇠 보정의 부정확성은 정량화 오류와 뇌 PET/MRI 연구에서 병변의 잘못된 판독으로 이어진다. 이 문제를 해결하기 위해, 자기장 불균일 보정을 포함한 다상 레벨셋 알고리즘에 기초한 개선된 초단파 에코 시간 MR-AC 방법을 제안하였다. 또한 CT-AC 및 PET/MRI 스캐너 제조업체가 제공한 MR-AC와 비교하여 레벨셋 기반 MR-AC 방법의 임상적 사용가능성을 평가하였다. 둘째로, MR-IGRT 시스템을 위한 심층 컨볼루션 신경망 모델을 사용하여 저필드 MR 이미지에서 생성된 합성 CT 방법를 제안하였다. 이 합성 CT 이미지를 변형 정합을 사용하여 생성된 변형 CT와 비교 하였다. 또한 골반, 흉부 및 복부 환자에서의 기하학적, 선량적 분석을 통해 방사선 치료계획에서의 합성 CT를 사용가능성을 평가하였다.Chapter 1. Introduction 1 1.1. Background 1 1.1.1. The Integration of MRI into Other Medical Devices 1 1.1.2. Chanllenges in the MRI Integrated System 4 1.1.3. Synthetic CT Generation 5 1.2. Purpose of Research 6 Chapter 2. MRI-based Attenuation Correction for PET/MRI 8 2.1. Background 8 2.2. Materials and Methods 10 2.2.1. Brain PET Dataset 19 2.2.2. MR-Based Attenuation Map using Level-Set Algorithm 12 2.2.3. Image Processing and Reconstruction 18 2.3. Results 20 2.4. Discussion 28 Chapter 3. MRI-based synthetic CT generation for MR-IGRT 30 3.1. Background 30 3.2. Materials and Methods 32 3.2.1. MR-dCT Paired DataSet 32 3.2.2. Synthetic CT Generation using 2D CNN 36 3.2.3. Data Analysis 38 3.3. Results 41 3.3.1. Image Comparison 41 3.3.2. Geometric Analysis 49 3.3.3. Dosimetric Analysis 49 3.4. Discussion 56 Chapter 4. Conclusions 59 Bibliography 60 Abstract in Korean (국문 초록) 64Docto

Incorporating Cardiac Substructures Into Radiation Therapy For Improved Cardiac Sparing

Growing evidence suggests that radiation therapy (RT) doses to the heart and cardiac substructures (CS) are strongly linked to cardiac toxicities, though only the heart is considered clinically. This work aimed to utilize the superior soft-tissue contrast of magnetic resonance (MR) to segment CS, quantify uncertainties in their position, assess their effect on treatment planning and an MR-guided environment. Automatic substructure segmentation of 12 CS was completed using a novel hybrid MR/computed tomography (CT) atlas method and was improved upon using a 3-dimensional neural network (U-Net) from deep learning. Intra-fraction motion due to respiration was then quantified. The inter-fraction setup uncertainties utilizing a novel MR-linear accelerator were also quantified. Treatment planning comparisons were performed with and without substructure inclusions and methods to reduce radiation dose to sensitive CS were evaluated. Lastly, these described technologies (deep learning U-Net) were translated to an MR-linear accelerator and a segmentation pipeline was created. Automatic segmentations from the hybrid MR/CT atlas was able to generate accurate segmentations for the chambers and great vessels (Dice similarity coefficient (DSC) \u3e 0.75) but coronary artery segmentations were unsuccessful (DSC\u3c0.3). After implementing deep learning, DSC for the chambers and great vessels was ≥0.85 along with an improvement in the coronary arteries (DSC\u3e0.5). Similar accuracy was achieved when implementing deep learning for MR-guided RT. On average, automatic segmentations required ~10 minutes to generate per patient and deep learning only required 14 seconds. The inclusion of CS in the treatment planning process did not yield statistically significant changes in plan complexity, PTV, or OAR dose. Automatic segmentation results from deep learning pose major efficiency and accuracy gains for CS segmentation offering high potential for rapid implementation into radiation therapy planning for improved cardiac sparing. Introducing CS into RT planning for MR-guided RT presented an opportunity for more effective sparing with limited increase in plan complexity

Incorporating Cardiac Substructures Into Radiation Therapy For Improved Cardiac Sparing

Growing evidence suggests that radiation therapy (RT) doses to the heart and cardiac substructures (CS) are strongly linked to cardiac toxicities, though only the heart is considered clinically. This work aimed to utilize the superior soft-tissue contrast of magnetic resonance (MR) to segment CS, quantify uncertainties in their position, assess their effect on treatment planning and an MR-guided environment. Automatic substructure segmentation of 12 CS was completed using a novel hybrid MR/computed tomography (CT) atlas method and was improved upon using a 3-dimensional neural network (U-Net) from deep learning. Intra-fraction motion due to respiration was then quantified. The inter-fraction setup uncertainties utilizing a novel MR-linear accelerator were also quantified. Treatment planning comparisons were performed with and without substructure inclusions and methods to reduce radiation dose to sensitive CS were evaluated. Lastly, these described technologies (deep learning U-Net) were translated to an MR-linear accelerator and a segmentation pipeline was created. Automatic segmentations from the hybrid MR/CT atlas was able to generate accurate segmentations for the chambers and great vessels (Dice similarity coefficient (DSC) \u3e 0.75) but coronary artery segmentations were unsuccessful (DSC\u3c0.3). After implementing deep learning, DSC for the chambers and great vessels was ≥0.85 along with an improvement in the coronary arteries (DSC\u3e0.5). Similar accuracy was achieved when implementing deep learning for MR-guided RT. On average, automatic segmentations required ~10 minutes to generate per patient and deep learning only required 14 seconds. The inclusion of CS in the treatment planning process did not yield statistically significant changes in plan complexity, PTV, or OAR dose. Automatic segmentation results from deep learning pose major efficiency and accuracy gains for CS segmentation offering high potential for rapid implementation into radiation therapy planning for improved cardiac sparing. Introducing CS into RT planning for MR-guided RT presented an opportunity for more effective sparing with limited increase in plan complexity

Statistical modeling of bladder motion and deformation in prostate cancer radiotherapy

Prostate cancer is the most common cancer amongst the male population in most developed countries. It is the most common cancer amongst the male population in France (73.609 cases in 2014) and in Colombia (9564 cases in 2014). It is also the third most common cause of cancer deaths in males in both countries (9.3% and 7.1% in France and in Colombia in 2014, respectively). One of the standard treatment methods is external radiotherapy, which involves delivering ionizing radiation to a clinical target, namely the prostate and seminal vesicles. Due to the uncertain location of organs during treatment, which involves around forty (40) radiation fractions delivering a total dose ranging from 70 to 80Gy, safety margins are defined around the tumor target upon treatment planning. The radiation units are expressed in Grays, abbreviated as Gy, which represents 1 Jule/Kg. This leads to portions of healthy organs neighboring the prostate or organs at risk – the bladder and rectum – to be included in the target volume, potentially resulting in adverse events affecting patients' urinary (hematuria and cystitis, among others) or rectal (rectal bleeding, fecal incontinence, etc.) functions. Several studies have shown that increasing dose delivery to the prostate leads to improved local cancer control, up to approximately 80Gy. However, such dose increases are limited by their associated risks of treatment-related toxicity involving the organs at risk. The bladder is notorious for presenting the largest inter-fraction shape variations during treatment, caused by continuous changes in volume. These variations in shape introduce geometric uncertainties that render assessment of the actual dose delivered to the bladder during treatment difficult, thereby leading to dose uncertainties that limit the possibility of modeling dose-volume response for late genitourinary (GU) toxicity. The Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC) project has stated that a similar dose-response to that of late gastrointestinal (GI) toxicity is far from being established. The dosimetric variables obtained from the planning CT prove to be very poor surrogates for the real delivered dose. As a result, it appears crucial to quantify uncertainties produced by inter-fraction bladder variations in order to determine dosimetric factors that affect late GU complications. The aim of this thesis was thus to characterize and predict uncertainties produced by geometric variations of the bladder between fractions, using solely the planning CT as input information. In clinical practice, a single CT scan is only available for a typical patient during the treatment planning while on-treatment CTs/CBCTs are seldom available. In this thesis, we thereby used a population approach to obtain enough data to learn the most important directions of bladder motion and deformation using principal components analysis (PCA). As in groundwork, these directions were then used to develop population-based models in order to predict and quantify geometrical uncertainties of the bladder. However, we use a longitudinal analysis in order to properly characterize both patient-specific variance and modes from the population. We proposed to use mixed-effects (ME) models and hierarchical PCA to separate intra and inter-patient variability to control confounding cohort effects. Other than using PCA, bladder shapes were represented by using spherical harmonics (SPHARM) which additionally enables data compression without losing information. Subsequently, we presented PCA models as a tool to quantify dose uncertainties produced by bladder motion and deformation between fractions. We then estimated mean and variance of the dose delivered to the bladder using PCA-based models via Monte Carlo simulation and dose integration; and subsequently, we compared the estimated accumulated doses with the accumulated dose derived from non-rigid registration and patient's available images. We also calculated average voxel doses, local dose variability and dose-volume histogram uncertainties.Resumen: El cáncer de próstata es el cáncer más común entre la población masculina en muchos de los países desarrollados. Especificamente, es el cáncer más común de la población masculina tanto en Francia (73.609 casos en 2014) como en Colombia (9564 casos en 2014); y además, es también la tercera causa de muerte por cancer en los hombres para ambos países (9.3% y 7.1% en Francia y en Colombia en 2014, respectivamente). Uno de los métodos de tratamiento más común es la radioterapia externa, el cual consiste en enviar una radiación ionizante a un objetivo clínico, en este caso la próstata y las vesículas seminales. Debido a incertidumbres producidas por variaciones anat\'omicas de los órganos durante el tratamiento, el cual consiste en 40 fracciones para un total de dosis entre 70 y 80 Gy, márgenes de seguridad son definidos alrededor del tumor durante la planeación del tratamiento. Lo anterior, conlleva a que partes de los órganos sanos, también llamados órganos en riesgo, que son cercanos a la próstata y vesículas seminales -como la vejiga y el recto- también sean irradiados, potencialmente resultando en eventos adversos que afectan las funciones urinarias (hematuria e infección urinaria) o rectal (sangrado rectal, incontinencia fecal) del paciente. Algunos estudios han demostrado que el incrementando la dosis a la próstata permite un mejor control local del cáncer (por encima de los 80Gy aproximadamente). Sin embargo, tales incrementos de dosis son limitados por sus riesgos asociados de toxicidad para los órganos en riesgo. La vejiga es particular por presentar las variaciones de forma más grandes entre las fracciones del tratamiento, las cuales son causadas por continuos cambios de volumen. Estas variaciones de forma de la vejiga introducen incertidumbres geométricas que hacen dif\'icil la determinacion de la verdadera dosis entregada a la vejiga durante el tratamiento. Estas incertidumbres limitan la posibilidad de modelar una relación dosis-volumen para la toxicidad Genitourinario tardia (GU). El proyecto Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC) ha establecido que una respuesta dosis-volumen similar a la que se tiene para la toxicidad Gastrointestinal tardia (GI) está lejos de ser establecida. Las variables dosimétricas que se obtienen de la tomografía computarizada de planeación pueden ser débilmente represenativas de la verdadera dosis suministrada. Por lo tanto, es crucial identificar las incertidumbres producidas por el movimiento y deformación entre fracciones de la vejiga con el fin de determinar los factores dosimétricos que afectan las complicaciones GU tardías. El propósito de esta tesis fue entonces caracterizar y predecir las incertidumbres geométricas producidas por las variaciones geométricas de la vejiga entre fracciones, usando solamente el CT de planeación como información de entrada. En la pr\'actica clínica, un sola tomografía computarizada esta disponible en la fase de planeación del tratamiento para un paciente típico, mientras que imágenes suplementarias durante el tratamiento estan raramente disponibles. Por lo tanto, en esta tesis fue usado un enfoque poblacional para obtener suficientes datos para aprender las direcciones más importantes de movimiento y deformación de la vejiga usando análisis de componentes principales (ACP). Tal como en trabajos anteriores, estas direcciones fueron también usadas para desarrollar modelos poblacionales para predecir y cuantificar incertidumbres geométricas de la vejiga. Sin embargo, en esta tesis se propuso un análisis longitudinal con el fin de adecuadamente caracterizar la varianza y los modos del paciente de la población. Básicamente, se propuso usar modelos de efectos-mixtos (ME) y ACP jerárquico para separar la variabilidad intra e inter-paciente para controlar efectos confusos de la población. Adicional a ACP, la superficie de la vejiga también fue representada usando esféricos harmónicos (SPHARM), lo cual nos permitio adicionalmente comprimir los datos sin perder información. Finalmente, se presentan los modelos basado en ACP como una herramienta para cuantificar las incertidumbres de la dosis producidas por el movimiento y deformación de la vejiga entre fracciones. El promedio y la varianza de la dosis entregada a la vejiga fueron estimadas utilizando modelos ACP via simulación de Monte Carlo e integración de la dosis; y posteriormente, se comparo las dosis acumuladas estimadas con la dosis acumulada obtenida a partir de registro no-rigido y las imagenes disponibles del paciente. Igualmente, se calcularon los valores promedio de la dosis por voxel, la variabilidad local de la dosis y las incertidumbres de los histogramas dosis-volumen.Résumé: Le cancer de la prostate est le cancer le plus fréquent chez les hommes dans la plupart des pays développés. C’est le cancer le plus fréquent chez les hommes en France (73.609 cas en 2014) et en Colombie (9564 cas en 2014). En outre, c’est la troisième cause de décès par cancer chez les hommes dans les deux pays (9,3 % en France et 7,1 % en Colombie en 2014). L’une des techniques de traitement est la radiothérapie externe, qui consiste à délivrer un rayonnement ionisant à une cible clinique, à savoir la prostate et les vésicules séminales. En raison des variations anatomiques au cours du traitement, qui consiste en environ 40 fractions de rayonnement délivrant une dose totale allant de 70 à 80Gy, des marges de sécurité sont définies autour de la cible tumorale lors de la planification du traitement. Ceci entraîne des portions d’organes sains voisins de la prostate - la vessie et le rectum - à être inclus dans le volume cible, pouvant conduire à des événements indésirables affectant les fonctions urinaires (hématurie et cystite, entre autres) ou rectale (saignement rectal, incontinence fécale, Etc.). Plusieurs études ont montré que l’augmentation de la dose administrée à la prostate conduit à une amélioration du contrôle local du cancer (jusqu’à environ 80Gy). Cependant, de telles augmentations de dose sont limitées par les risques associés de toxicité pour les organes à risque. La vessie présente les plus grandes variations de forme entre fractions de traitement, provoquées par des changements continus de volume. Ces variations de forme introduisent des incertitudes géométriques qui rendent difficile l’évaluation de la dose réellement délivrée à la vessie pendant le traitement. Ces incertitudes limitent la possibilité de modéliser une relation dose-volume pour la toxicité génito-urinaire tardive (GU). Le projet QUANTEC (Quantitative Analysis of Normal Tissue Effects in the Clinic) a déclaré que la relation dose-réponse pour la toxicité gastro-intestinale tardive (GI) était loin d’être établie. Les variables dosimétriques obtenues à partir de la tomodensitométrie de planification peuvent être faiblement représentative de la dose effectivement administrée. En conséquence, il est crucial de quantifier les incertitudes produites par les variations inter-fraction de la vessie afin de déterminer les facteurs dosimétriques qui affectent les complications GU tardives. Le but de cette thèse était donc de caractériser et de prédire les incertitudes produites par les variations géométriques de la vessie entre les fractions de traitement, en utilisant uniquement la tomodensitométrie de planification comme information d’entrée. En pratique clinique, une seule tomodensitométrie est disponible au moment de la planification du traitement pour un patient typique, alors que des images supplémentaires peuvent être acquises en cours de traitement. Dans cette thèse une approche population a été utilisée pour obtenir suffisamment de données pour apprendre les directions les plus importantes du mouvement et de la déformation de la vessie en utilisant l’analyse en composante principales (ACP). Comme dans les travaux de référence, ces directions ont ensuite été utilisées pour développer des modèles basés population pour prédire et quantifier les incertitudes géométriques de la vessie. Cependant, nous avons utilisé une analyse longitudinale afin de caractériser correctement la variance du patient et les modes spécifiques du patient à partir de la population. Nous avons proposé d’utiliser un modèle à effets mixtes (ME) et une ACP hiérarchique pour séparer la variabilité intra et inter-patients afin de contrôler les effets de cohorte confondus. Outre l’ACP, la forme de la vessie a été représentée par l’utilisation d’harmoniques sphériques (SPHARM), ce qui a permis la compression des données sans perte d’information. Par la suite, nous avons cherché à quantifier les incertitudes de dose produites par le mouvement de la vessie et la déformation entre les fractions. Finalement, nous avons présenté des modèles sur l’APC comme un outil pour quantifier des incertitudes de la dose produit par le mouvement et déformation de la vessie entre fractions. Nous avons ensuite estimé la moyenne et la variance de la dose délivrée à la vessie en utilisant des modèles basés sur l’APC par la méthode de simulation de Monte-Carlo et l’intégrale de la dose; et par la suite, nous avons comparé les doses cumulées estimées avec la dose accumulée obtenue en utilisant un recalage d’image non-rigide et les images du patient. Également, nous avons calculé la moyenne de la dose par voxel, la variabilité de la dose locale et des incertitudes d’histogramme de volume de dose.Doctorad

RANGE ADAPTIVE PROTON THERAPY FOR PROSTATE CANCER

Purpose: The rapid distal falloff of a proton beam allows for sparing of normal tissues distal to the target. However proton beams that aim directly towards critical structures are avoided due to concerns of range uncertainties, such as CT number conversion and anatomy variations. We propose to eliminate range uncertainty and enable prostate treatment with a single anterior beam by detecting the proton’s range at the prostate-rectal interface and adaptively adjusting the range in vivo and in real-time. Materials and Methods: A prototype device, consisting of an endorectal liquid scintillation detector and dual-inverted Lucite wedges for range compensation, was designed to test the feasibility and accuracy of the technique. Liquid scintillation filled volume was fitted with optical fiber and placed inside the rectum of an anthropomorphic pelvic phantom. Photodiode-generated current signal was generated as a function of proton beam distal depth, and the spatial resolution of this technique was calculated by relating the variance in detecting proton spills to its maximum penetration depth. The relative water-equivalent thickness of the wedges was measured in a water phantom and prospectively tested to determine the accuracy of range corrections. Treatment simulation studies were performed to test the potential dosimetric benefit in sparing the rectum. Results: The spatial resolution of the detector in phantom measurement was 0.5 mm. The precision of the range correction was 0.04 mm. The residual margin to ensure CTV coverage was 1.1 mm. The composite distal margin for 95% treatment confidence was 2.4 mm. Planning studies based on a previously estimated 2mm margin (90% treatment confidence) for 27 patients showed a rectal sparing up to 51% at 70 Gy and 57% at 40 Gy relative to IMRT and bilateral proton treatment. Conclusion: We demonstrated the feasibility of our design. Use of this technique allows for proton treatment using a single anterior beam, significantly reducing the rectal dose

Artefact Reduction Methods for Iterative Reconstruction in Full-fan Cone Beam CT Radiotherapy Applications

A cone beam CT (CBCT) system acquires two-dimensional projection images of an imaging object from multiple angles in one single rotation and reconstructs the object geometry in three dimensions for volumetric visualization. It is mounted on most modern linear accelerators and is routinely used in radiotherapy to verify patient positioning, monitor patient contour changes throughout the course of treatment, and enable adaptive radiotherapy planning. Iterative image reconstruction algorithms use mathematical methods to iteratively solve the reconstruction problem. Iterative algorithms have demonstrated improvement in image quality and / or reduction in imaging dose over traditional filtered back-projection (FBP) methods. However, despite the advancement in computer technology and growing availability of open-source iterative algorithms, clinical implementation of iterative CBCT has been limited. This thesis does not report development of codes for new iterative image reconstruction algorithms. It focuses on bridging the gap between the algorithm and its implementation by addressing artefacts that are the results of imperfections from the raw projections and from the imaging geometry. Such artefacts can severely degrade image quality and cannot be removed by iterative algorithms alone. Practical solutions to solving these artefacts will be presented and this in turn will better enable clinical implementation of iterative CBCT reconstruction

Image Guided Respiratory Motion Analysis: Time Series and Image Registration.

The efficacy of Image guided radiation therapy (IGRT) systems relies on accurately extracting, modeling and predicting tumor movement with imaging techniques. This thesis investigates two key problems associated with such systems: motion modeling and image processing. For thoracic and upper abdominal tumors, respiratory motion is the dominant factor for tumor movement. We have studied several special structured time series analysis techniques to incorporate the semi-periodicity characteristics of respiratory motion. The proposed methods are robust towards large variations among fractions and populations; the algorithms perform stably in the presence of sparse radiographic observations with noise. We have proposed a subspace projection method to quantitatively evaluate the semi-periodicity of a given observation trace; a nonparametric local regression approach for real-time prediction of respiratory motion; a state augmentation scheme to model hysteresis; and an ellipse tracking algorithm to estimate the trend of respiratory motion in real time. For image processing, we have focused on designing regularizations to account for prior information in image registration problems. We investigated a penalty function design that accommodates tissue-type-dependent elasticity information. We studied a class of discontinuity preserving regularizers that yield smooth deformation estimates in most regions, yet allow discontinuities supported by data. We have further proposed a discriminate regularizer that preserves shear discontinuity, but discourages folding or vacuum generating flows. In addition, we have initiated a preliminary principled study on the fundamental performance limit of image registration problems. We proposed a statistical generative model to account for noise effect in both source and target images, and investigated the approximate performance of the maximum-likelihood estimator corresponding to the generative model and the commonly adopted M-estimator. A simple example suggests that the approximation is reasonably accurate. Our studies in both time series analysis and image registration constitute essential building-blocks for clinical applications such as adaptive treatment. Besides their theoretical interests, it is our sincere hope that with further justifications, the proposed techniques would realize its clinical value, and improve the quality of life for patients.Ph.D.Electrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60673/1/druan_1.pd