Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/33385
Title: Nonrigid Registration Methods for Myocardial Perfusion Mri and Cerebral Diffusion Tensor Mri
Authors: LI CHAO
Keywords: Image registration, image segmentation, perfusion MRI, diffusion tensor MRI, myocardium, neuroimage
Issue Date: 16-Jan-2012
Source: LI CHAO (2012-01-16). Nonrigid Registration Methods for Myocardial Perfusion Mri and Cerebral Diffusion Tensor Mri. ScholarBank@NUS Repository.
Abstract: As the two leading causes of death, ischemic heart disease and cerebrovascular disease are of great research importance. Perfusion Magnetic Resonance Imaging (MRI) and diffusion MRI have emerged as the most effective non-invasive diagnostic tools respectively for ischemic heart disease and cerebral ischemic small vessel disease. This thesis discusses the nonrigid registration problems in these two imaging techniques. To compensate patients' breathing and precisely trace the perfusion signal over time, nonrigid registration of the perfusion sequence is required. This registration was conventionally accomplished by pairwisely mapping images from different perfusion phases but it often failed to handle the great mismatch of intensity distributions between the reference and floating images due to the variation of the contrast concentration. We propose to register the observed sequence to a pseudo ground truth (PGT), which is a motion/noise free sequence that is estimated from the observed one, and having almost identical intensity variations as the original sequence. In contrast to pairs of images within the observed sequence, the corresponding pairs of images between the observed sequence and the PGT have similar intensities, and thus the registration problem is greatly eased. Our experimental results on 20 cardiac perfusion MR scans have quantitatively and qualitatively shown that the method is able to effectively compensate for the elastic deformation of the heart in the myocardial perfusion sequence. The state-of-the-art DTI analysis frameworks, e.g., Voxel-Based Morphometry and Tract-Based Spatial Statistics, are based on image-to-image registration and cannot analyze brain fiber tracts. The brain fiber tracts reconstruction, i.e., tractography, is usually accomplished by linking the principal directions of diffusion tensors, which often early terminates at white matter (WM) lesion regions. Besides, tractography segmentation and establishing correspondences among fiber tracts are challenging. We propose a novel fiber-to-DTI registration method which deforms a manually annotated whole brain fiber model to diffusion tensor images of new subjects. Tractography, tractography segmentation, and inter-subject fiber correspondences are automatically obtained by this registration. The early termination issue is overcome by imposing inter- and intra- fiber regularization. To handle severe WM lesions, we use robust principal component analysis to identify regions with unreliable registration, and propose a statistical along-fiber prior to automatically rectify the registration of these regions. Experimental results have shown successful registration on 55 subjects and the registration is robust to WM lesions. The DTI measure computed from registered anatomical fiber bundles have significant correlation with cognitive functions.
URI: http://scholarbank.nus.edu.sg/handle/10635/33385
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