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UBC Theses and Dissertations

Prostate registration using magnetic resonance elastography for cancer localization Nir, Guy


Noninvasive detection and localization of prostate cancer in medical imaging is an important, yet difficult task. Benefits range from diagnosis of cancer, to planning and guidance of its treatment. In order to characterize cancer and evaluate its localization in volumetric images, such as ultrasound or magnetic resonance imaging (MRI), their spatial correspondence with the "gold standard" provided by histopathology must be established. In this thesis, we propose a general framework for a multi-slice to volume registration that is applied to register a stack of sparse, unaligned two-dimensional histological slices to a three-dimensional volumetric imaging of the prostate. The approach uses particle filtering that allows deriving optimal pose parameters of the slices in a Bayesian approach. We then propose a novel registration method between in vivo and ex vivo MRI of the prostate to facilitate its registration to histopathology. The method incorporates elasticity information, acquired by magnetic resonance elastography (MRE), to generate a patient-specific biomechanical model of the prostate and periprostatic tissue. Next, we propose a registration method between preoperative MRI and intraoperative transrectal-ultrasound. The method can be incorporated with a robotic surgical system to augment the surgeon's visualization during robot-assisted prostatectomy. We also study the use of elasticity-based registration of ultrasound elastography and MRE. We then present an image processing approach for enhancing MRE data. The approach employs registration to compensate for motion of patients during the scan to improve the accuracy of the reconstructed elastogram. A super-resolution technique is employed to increase the resolution of the acquired images by utilizing unique properties of MRE. Finally, we develop a theory for optimization-based design of motion encoding in MRE that allows reducing scanning time and increasing signal-to-noise ratio of elasticity reconstruction. We formulate the displacement estimation of the mechanical wave as an experimental design problem, by which we quantify performance of sequences, and optimize multidirectional designs. The proposed methods have been evaluated in simulations and on a diverse set of clinical data. Results may pave the way for a broader clinical deployment of elastography and elastography-based image processing.

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