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Document Type : Latin Dissertation
Language of Document : English
Record Number : 150859
Doc. No : ET22651
Main Entry : Shital Bipin Desai
Title Proper : Noise and Error Propagation in Diffusion Tensor Imaging
Note : This document is digital این مدرک بصورت الکترونیکی می باشد
Abstract : Diffusion Tensor Imaging (DTI) is a unique non-invasive technique for in vivovisualization and analysis of white matter fiber tracts by measuring the anisotropy ofwater molecule diffusion in the brain tissue. DTI has been used to highlight white matterto demonstrate subtle abnormalities in neurological disorders such as stroke, dyslexia,multiple sclerosis etc. and is currently being used increasingly in clinical imagingprotocols. Diffusion weighted images are affected by several artifacts and noise from thehuman subject and the MRI scanner. This thesis studies the error propagation in thecalculation of the DTI invariant anisotropy. The main focus is on the computation ofFractional Anisotropy (FA) across an image volume using four different methods andquantitatively comparing them in terms of error propagation, filtering and computationalefficiency in data sets containing either simulated or human brain data. Simulated data isan important contribution in this thesis as they serve as a platform to validate the FAresults for human brain MRI data. The four methods that were used were DiffusionTensor, Diffusion Ellipsoid, Hasan and Platonic Variance. The results showed similartrends across the simulated and real data sets for all methods. Of the four methods used tocalculate FA, the Hasan method without diffusion tensor yielded best efficiency, in termsof computation time, but exhibited poor noise....-...,..tested for theQ1 PC1 bus cardBoth these projects mere sofixare des elopment efforts tonards contributing to dlfferentaspects of Roboucs and lZ1echatronics projects m the Controls and Roboucs Group..
Subject : Electericl tess
: برق
electronic file name : TL45877.pdf
Title and statement of responsibility and : Noise and Error Propagation in Diffusion Tensor Imaging [Thesis]
 
 
 
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