Optimizing Magnetic Resonance Imaging for Image-Guided Radiotherapy

Magnetic resonance imaging (MRI) is playing an increasingly important role in image-guided radiotherapy. MRI provides excellent soft tissue contrast, and is flexible in characterizing various tissue properties including relaxation, diffusion and perfusion. This thesis aims at developing new image analysis and reconstruction algorithms to optimize MRI in support of treatment planning, target delineation and treatment response assessment for radiotherapy. First, unlike Computed Tomography (CT) images, MRI cannot provide electron density information necessary for radiation dose calculation. To address this, we developed a synthetic CT generation algorithm that generates pseudo CT images from MRI, based on tissue classification results on MRI for female pelvic patients. To improve tissue classification accuracy, we learnt a pelvic bone shape model from a training dataset, and integrated the shape model into an intensity-based fuzzy c-menas classification scheme. The shape-regularized tissue classification algorithm is capable of differentiating tissues that have significant overlap in MRI intensity distributions. Treatment planning dose calculations using synthetic CT image volumes generated from the tissue classification results show acceptably small variations as compared to CT volumes. As MRI artifacts, such as B1 filed inhomogeneity (bias field) may negatively impact the tissue classification accuracy, we also developed an algorithm that integrates the correction of bias field into the tissue classification scheme. We modified the fuzzy c-means classification by modeling the image intensity as the true intensity corrupted by the multiplicative bias field. A regularization term further ensures the smoothness of the bias field. We solved the optimization problem using a linearized alternating direction method of multipliers (ADMM) method, which is more computational efficient over existing methods. The second part of this thesis looks at a special MR imaging technique, diffusion-weighted MRI (DWI). By acquiring a series of DWI images with a wide range of b-values, high order diffusion analysis can be performed using the DWI image series and new biomarkers for tumor grading, delineation and treatment response evaluation may be extracted. However, DWI suffers from low signal-to-noise ratio at high b-values, and the multi-b-value acquisition makes the total scan time impractical for clinical use. In this thesis, we proposed an accelerated DWI scheme, that sparsely samples k-space and reconstructs images using a model-based algorithm. Specifically, we built a 3D block-Hankel tensor from k-space samples, and modeled both local and global correlations of the high dimensional k-space data as a low-rank property of the tensor. We also added a phase constraint to account for large phase variations across different b-values, and to allow reconstruction from partial Fourier acquisition, which further accelerates the image acquisition. We proposed an ADMM algorithm to solve the constrained image reconstruction problem. Image reconstructions using both simulated and patient data show improved signal-to-noise ratio. As compared to clinically used parallel imaging scheme which achieves a 4-fold acceleration, our method achieves an 8-fold acceleration. Reconstructed images show reduced reconstruction errors as proved on simulated data and similar diffusion parameter mapping results on patient data.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143919/1/llliu_1.pd

Restauration d'images en IRM anatomique pour l'étude préclinique des marqueurs du vieillissement cérébral

Les maladies neurovasculaires et neurodégénératives liées à l'âge sont en forte augmentation. Alors que ces changements pathologiques montrent des effets sur le cerveau avant l'apparition de symptômes cliniques, une meilleure compréhension du processus de vieillissement normal du cerveau aidera à distinguer l'impact des pathologies connues sur la structure régionale du cerveau. En outre, la connaissance des schémas de rétrécissement du cerveau dans le vieillissement normal pourrait conduire à une meilleure compréhension de ses causes et peut-être à des interventions réduisant la perte de fonctions cérébrales associée à l'atrophie cérébrale. Par conséquent, ce projet de thèse vise à détecter les biomarqueurs du vieillissement normal et pathologique du cerveau dans un modèle de primate non humain, le singe marmouset (Callithrix Jacchus), qui possède des caractéristiques anatomiques plus proches de celles des humains que de celles des rongeurs. Cependant, les changements structurels (par exemple, de volumes, d'épaisseur corticale) qui peuvent se produire au cours de leur vie adulte peuvent être minimes à l'échelle de l'observation. Dans ce contexte, il est essentiel de disposer de techniques d'observation offrant un contraste et une résolution spatiale suffisamment élevés et permettant des évaluations détaillées des changements morphométriques du cerveau associé au vieillissement. Cependant, l'imagerie de petits cerveaux dans une plateforme IRM 3T dédiée à l'homme est une tâche difficile car la résolution spatiale et le contraste obtenus sont insuffisants par rapport à la taille des structures anatomiques observées et à l'échelle des modifications attendues. Cette thèse vise à développer des méthodes de restauration d'image pour les images IRM précliniques qui amélioreront la robustesse des algorithmes de segmentation. L'amélioration de la résolution spatiale des images à un rapport signal/bruit constant limitera les effets de volume partiel dans les voxels situés à la frontière entre deux structures et permettra une meilleure segmentation tout en augmentant la reproductibilité des résultats. Cette étape d'imagerie computationnelle est cruciale pour une analyse morphométrique longitudinale fiable basée sur les voxels et l'identification de marqueurs anatomiques du vieillissement cérébral en suivant les changements de volume dans la matière grise, la matière blanche et le liquide cérébral.Age-related neurovascular and neurodegenerative diseases are increasing significantly. While such pathological changes show effects on the brain before clinical symptoms appear, a better understanding of the normal aging brain process will help distinguish known pathologies' impact on regional brain structure. Furthermore, knowledge of the patterns of brain shrinkage in normal aging could lead to a better understanding of its causes and perhaps to interventions reducing the loss of brain functions. Therefore, this thesis project aims to detect normal and pathological brain aging biomarkers in a non-human primate model, the marmoset monkey (Callithrix Jacchus) which possesses anatomical characteristics more similar to humans than rodents. However, structural changes (e.g., volumes, cortical thickness) that may occur during their adult life may be minimal with respect to the scale of observation. In this context, it is essential to have observation techniques that offer sufficiently high contrast and spatial resolution and allow detailed assessments of the morphometric brain changes associated with aging. However, imaging small brains in a 3T MRI platform dedicated to humans is a challenging task because the spatial resolution and the contrast obtained are insufficient compared to the size of the anatomical structures observed and the scale of the xpected changes with age. This thesis aims to develop image restoration methods for preclinical MR images that will improve the robustness of the segmentation algorithms. Improving the resolution of the images at a constant signal-to-noise ratio will limit the effects of partial volume in voxels located at the border between two structures and allow a better segmentation while increasing the results' reproducibility. This computational imaging step is crucial for a reliable longitudinal voxel-based morphometric analysis and for the identification of anatomical markers of brain aging by following the volume changes in gray matter, white matter and cerebrospinal fluid

Multimodal Image Fusion and Its Applications.

Image fusion integrates different modality images to provide comprehensive information of the image content, increasing interpretation capabilities and producing more reliable results. There are several advantages of combining multi-modal images, including improving geometric corrections, complementing data for improved classification, and enhancing features for analysis...etc. This thesis develops the image fusion idea in the context of two domains: material microscopy and biomedical imaging. The proposed methods include image modeling, image indexing, image segmentation, and image registration. The common theme behind all proposed methods is the use of complementary information from multi-modal images to achieve better registration, feature extraction, and detection performances. In material microscopy, we propose an anomaly-driven image fusion framework to perform the task of material microscopy image analysis and anomaly detection. This framework is based on a probabilistic model that enables us to index, process and characterize the data with systematic and well-developed statistical tools. In biomedical imaging, we focus on the multi-modal registration problem for functional MRI (fMRI) brain images which improves the performance of brain activation detection.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120701/1/yuhuic_1.pd

Fast and robust hybrid framework for infant brain classification from structural MRI : a case study for early diagnosis of autism.

The ultimate goal of this work is to develop a computer-aided diagnosis (CAD) system for early autism diagnosis from infant structural magnetic resonance imaging (MRI). The vital step to achieve this goal is to get accurate segmentation of the different brain structures: whitematter, graymatter, and cerebrospinal fluid, which will be the main focus of this thesis. The proposed brain classification approach consists of two major steps. First, the brain is extracted based on the integration of a stochastic model that serves to learn the visual appearance of the brain texture, and a geometric model that preserves the brain geometry during the extraction process. Secondly, the brain tissues are segmented based on shape priors, built using a subset of co-aligned training images, that is adapted during the segmentation process using first- and second-order visual appearance features of infant MRIs. The accuracy of the presented segmentation approach has been tested on 300 infant subjects and evaluated blindly on 15 adult subjects. The experimental results have been evaluated by the MICCAI MR Brain Image Segmentation (MRBrainS13) challenge organizers using three metrics: Dice coefficient, 95-percentile Hausdorff distance, and absolute volume difference. The proposed method has been ranked the first in terms of performance and speed

Development of an image processing pipeline for the study of corticol lesions in multiple sclerosis patients using ultra-high field MRI

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Biofísica Médica e Fisiologia de Sistemas), Universidade de Lisboa, Faculdade de Ciências, 2019A esclerose múltipla é uma doença crónica e inflamatória do sistema nervoso central de alta prevalência nos dias de hoje. Durante anos, o foco da doença foi a patologia visível na matéria branca. Apesar dos primeiros estudos de patologia cortical em esclerose múltipla apontarem para a década de 60, foi apenas no início do novo século que o córtex passou a ser estudado como parte integral da doença. Desde então, estudos têm vindo a demonstrar que o comprometimento do córtex parece estar relacionado com danos cognitivos e físicos, frequentemente associados à doença. A necessidade de melhor compreender o impacto das lesões corticais no desenvolvimento da doença e na vida diária destes pacientes tem motivado o seu estudo, sendo a Ressonância Magnética (RM), em particular scanners de campo ultra-alto, a melhor ferramenta para as detetar e estudar. A melhoria da razão sinal-ruído e da resolução espacial dos scanners de RM de campo ultra-alto tem permitido o aumento da deteção de lesões corticais. Ainda assim, a sua sensibilidade continua a não ser ideal e a estar fortemente dependente do tipo de lesão cortical, do contraste de RM usado na sua deteção e da existência de ferramentas robustas que permitam a sua deteção de modo automático, mais eficiente e com menor espaço para erro. A falta de marcadores de imagem para a remielinização ou desmielinização parcial, tal como a ausência de diretrizes para a deteção destas lesões com campos de 7 (T)esla parece explicar a dificuldade em distinguir e identificar falsos positivos e as diferenças encontradas nas deteções realizadas por diferentes avaliadores. Uma desvantagem dos scanners de campo ultra-alto é o maior efeito de bias que, caso não seja removido aquando da aquisição de imagens, terá de ser removido na fase de processamento por softwares e algoritmos que não estão originalmente construídos para trabalhar com imagens de maior resolução e cuja prestação não está ainda bem explorada nestas condições. Estes desafios comprometem o potencial dos scanners de RM de campo ultra-alto para o estudo das lesões corticais na esclerose múltipla. Este projeto procura desenvolver uma pipeline semiautomática para o pré-processamento e processamento de imagens de RM de cariz estrutural de doentes com esclerose múltipla obtidas num scanner de campo ultra-alto. A pipeline é criada de modo gradual, recorrendo a análises visuais, ou de outro tipo, para confirmar a qualidade de cada passo antes de avançar para o seguinte, no pressuposto de que a qualidade dos softwares de imagem comercialmente disponíveis será menor ao utilizar imagens de maior resolução. A ocorrência de lesões corticais no córtex sensório-motor (SM1) é igualmente determinada e usada para validar a qualidade da pipeline. Doze doentes com esclerose múltipla na sua forma recidivante-remitente ou secundariamente progressiva e seis controlos foram incluídos neste projeto. Todas as permissões necessárias do comité local de ética, proteção de dados e da Danish Medicines Agency foram previamente obtidas. Os doentes foram estudados num scanner de RM de corpo inteiro da Philips, Achieva 7,0 T, dedicado a investigação. Os participantes foram observados usando quatro tipos distintos de contraste: magnetization prepared rapid acquisition by gradient echo (MPRAGE) a três dimensões (3D) com 0,65-mm de resolução isotrópica, 3D fluid attenuated inversion recovery (FLAIR) com 0,7-mm de resolução isotrópica, 3D T1-weighted (T1w) de resolução 0,85x0,85x1,0 mm3 e 3D T2-weighted Turbo Spin Echo (T2w-TSE) de 0,4-mm de resolução isotrópica. A vertente de pré-processamento da pipeline incluiu uma correção de bias e o co-registo de imagens. Para a correção de bias, o software SPM foi testado utilizando os parâmetros habituais e uma alteração dos parâmetros relativos à smoothness e regularização, como sugerido na literatura. O processo de co-registo seguiu o procedimento utilizado no processamento de imagens de doentes com esclerose múltipla de 3 T no Danish Research Centre for Magnetic Resonance (DRCMR), com alterações posteriormente adicionadas para melhorar a qualidade do alinhamento das imagens de cada indivíduo a 7 T. Após o pré-processamento, uma deteção de lesões corticais, seguida da sua segmentação, foi realizada manualmente utilizando as ferramentas do software FSL. A vertente de processamento da pipeline incluiu uma segmentação do cérebro, um registo das imagens dos doentes e a criação de superfícies corticais. A segmentação foi testada utilizando três diferentes ferramentas: o software SPM, uma toolbox do SPM, CAT, e a ferramenta de segmentação do FSL, FAST. A toolbox do SPM, DARTEL, foi usada no registo de imagens e o software FreeSurfer permitiu a criação de superfícies individuais e de grupo no último passo da pipeline. As máscaras com as lesões criadas após a segmentação manual de lesões seguiram um caminho semelhante de processamento de modo a permitir a sua correta sobreposição no respetivo volume, e, posteriormente, superfície, e a possibilidade de fazer análises individuais ou de grupo. Os resultados obtidos mostraram que os softwares para processamento de imagens de RM disponíveis apresentam, em geral, uma boa prestação e fornecem resultados de confiança. Ainda assim, a sua prestação pode ser otimizada incluindo procedimentos adicionais em cada passo ou por alteração das configurações originais dos softwares. A diminuição do parâmetro de largura à meia altura com um aumento do parâmetro de regularização na correção de bias com o SPM permitiu a criação de campos de bias mais fieis às imagens originais, consequentemente melhorando a sua correção e a diferenciação da matéria branca e matéria cinzenta nas imagens resultantes. A criação adicional de máscaras contendo apenas o cérebro e a utilização exclusiva de transformações de corpo rígido no co-registo de imagens permitiu a utilização de vários contrastes na tarefa de deteção de lesões, sem interferir com a sua localização ou morfologia. Na segmentação, a toolbox do SPM, CAT, mostrou melhorias na capacidade de separar as diferentes classes de tecidos com maior confiança e qualidade, particularmente nas regiões de contacto entre a matéria branca e cinzenta. Consequentemente, a qualidade do alinhamento das imagens dos diferentes doentes e a posterior criação de uma imagem média a partir de imagens individuais foi melhorada. O sucesso da pipeline permitiu a sobreposição das lesões corticais manualmente segmentadas nas superfícies individuais e/ou comuns criadas, onde foi descoberto que a maioria das lesões ocorreu no hemisfério direito, com sobreposições de lesões respetivas a diferentes doentes a ocorrer maioritariamente nos sulcos corticais, comparativamente aos giros. Porém, a segmentação de lesões demonstrou ser dispendiosa, dependente do avaliador e altamente influenciada por fatores inerentes ao avaliador, tal como o cansaço, nível de concentração ou de aborrecimento, e fatores externos, no qual se destacam a luminosidade do computador ou a luminosidade da sala onde a deteção foi feita. A feature do FreeSurfer para imagens de maior resolução não se mostrou fiável no tratamento dos dados de resolução isotrópica de 0,5-mm deste projeto, uma possível razão pela qual ainda se encontra em desenvolvimento. Apesar dos bons resultados obtidos, investigação adicional será necessária para melhor compreender a prestação destes e de outros softwares para imagem médica no processamento de imagens de RM de maior resolução, tal como a melhor maneira de tirar partido dos mesmos em estudos clínicos a 7 T. A extensão da pipeline a outros doentes com esclerose múltipla irá aumentar a amostra em estudo e permitir um estudo mais extensivo da patologia cortical e a compreensão do impacto de uma ou mais lesões localizadas na região SM1 na conectividade e integridade funcional da região cortical afetada.The importance of grey matter pathology to the understanding of multiple sclerosis has been acknowledged. However, the sensitivity to cortical lesions is limited when using conventional magnetic resonance imaging (MRI) systems. Ultra-high field (UHF) MRI systems have improved detection sensitivity but impose the additional challenge of a higher effect of bias to account for. Currently, image processing tools are not designed for higher resolution data and the performance of common software packages under these conditions has not been properly explored. These challenges have impaired the potential of UHF-MRI to study cortical lesions in multiple sclerosis. This project aims at developing a semi-automated pipeline for the pre-processing and processing of structural UHF-MRI data of multiple sclerosis patients. The pipeline is built in a step-by-step fashion, making use of visual assessments and other analyses to confirm the quality of each step before advancing to the next, under the assumption that the performance of common imaging software packages will be poorer when using higher resolution data. The occurrence of cortical lesions within the primary sensory-motor cortex (SM1) is also determined and used to validate the quality of the pipeline. Twelve patients with relapsing-remitting multiple sclerosis or secondary progressive multiple sclerosis and six healthy age-matched controls were included in this project. All relevant permissions from the local ethics committee and data protection had been obtained beforehand. All participants were studied with whole-brain ultra-high field MRI at 7 Tesla (T), using a research-only 7 T Achieva MR system. The participants were scanned using four different MRI modalities, namely 3-dimensional (3D) magnetization prepared rapid acquisition by gradient echo (MPRAGE) at 0.65-mm isotropic resolution, 3D fluid attenuated inversion recovery (FLAIR) at 0.7-mm isotropic resolution, 3D T1-weighted (T1w) of 0.85x0.85x1.0 mm3 reconstructed resolution and 3D T2-weighted Turbo Spin Echo (T2w-TSE) at 0.4-mm isotropic reconstructed resolution. The pre-processing pipeline included a bias correction and a coregistration step. For the bias correction, SPM was tested using its default parameters and an alternative configuration that altered the smoothness and regularization parameters. The coregistration followed an approach used in the processing of multiple sclerosis data at 3 T, with changes added to improve the quality of the within-subject alignment at 7 T. After the data pre-processing, manual detection and segmentation of cortical lesions was performed using FSLeyes. The processing pipeline included brain segmentation, subject registration and cortical surface creation. Brain segmentation was tested with SPM, with SPM’s toolbox, CAT, and with FSL’s segmentation tool, FAST. SPM’s DARTEL tool was used for subject registration and FreeSurfer allowed the creation of individual and an average cortical surface. The lesion masks created after the manual segmentation task followed a similar processing route to allow their overlay on the respective brain volumes and, posteriorly, surfaces, and the possibility of individual and group analyses. Results showed that the currently available MRI image processing tools present overall good performance and reliability in the processing of higher resolution data of multiple sclerosis patients. Still, the quality of the outcomes can be optimized by including additional steps or changes to the original software configurations. Modifying SPM’s smoothness and regularization parameters for the estimation of bias minimized its effect in the data, allowing a better differentiation between grey matter and white matter. Removing the skull whilst keeping the coregistration to rigid body transformations allowed the use of several contrasts in the lesion detection task without interfering with the lesions’ morphology and topography. Brain segmentation using CAT showed more stability across the dataset, improving the quality of the subsequent subject registration and consequently of the average brain created. The success of the pipeline led to the possibility of overlaying the manually segmented lesions on the individual and group surfaces where it was found that the majority of lesions occurred on the right hemisphere and that lesion overlaps were more common in cortical sulci. Despite the results obtained, further research is needed to understand the performance of other software packages in the processing of higher resolution MRI data and how to fully exploit these tools in the study of clinical data at 7 T

Image processing methods for human brain connectivity analysis from in-vivo diffusion MRI

In this PhD Thesis proposal, the principles of diffusion MRI (dMRI) in its application to the human brain mapping of connectivity are reviewed. The background section covers the fundamentals of dMRI, with special focus on those related to the distortions caused by susceptibility inhomogeneity across tissues. Also, a deep survey of available correction methodologies for this common artifact of dMRI is presented. Two methodological approaches to improved correction are introduced. Finally, the PhD proposal describes its objectives, the research plan, and the necessary resources

Probabilistic partial volume modelling of biomedical tomographic image data

EThOS - Electronic Theses Online ServiceGBUnited Kingdo