Optimizing Magnetic Resonance Imaging for Image-Guided Radiotherapy
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Magnetic resonance imaging (MRI) is playing an increasingly important role in providing imaging
guidance to radiotherapy. 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. To improve classification accuracy, we proposed a shape-regularized fuzzy clustering algorithm, where a shape model was learnt from a training dataset and integrated into an intensity-based tissue classification scheme. The second part of our work looks at a special MR imaging technique, diffusion-weighted MRI (DWI), which is promising for tumor delineation and grading, but suffers from long acquisition time and low signal-to-noise ratio. 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.