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Diffusion weighted image reconstruct...

Farrah, Mustafa.

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Diffusion weighted image reconstruction: A Bayesian approach.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Diffusion weighted image reconstruction: A Bayesian approach./
Author:	Farrah, Mustafa.
Description:	125 p.
Notes:	Source: Dissertation Abstracts International, Volume: 76-11(E), Section: B.
Contained By:	Dissertation Abstracts International76-11B(E).
Subject:	Electrical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3709122
ISBN:	9781321846133

Diffusion weighted image reconstruction: A Bayesian approach.
Farrah, Mustafa.

Diffusion weighted image reconstruction: A Bayesian approach. - 125 p.

Source: Dissertation Abstracts International, Volume: 76-11(E), Section: B.

Thesis (Ph.D.)--The University of Wisconsin - Milwaukee, 2015.

Diffusion weighted imaging (DWI) is a form of Magnetic Resonance Imaging (MRI) based upon measuring the random Brownian motion of water molecules within a voxel of tissue. Recently, DWI has become an important modality in the diagnostic work-up of early identification of ischemic stroke, differentiation of acute stroke from other conditions that mimic it, differentiation of epidermoid cyst from arachnoid cyst, assessment of cortical lesions in Creutzfeldt Jakob disease(CJD), assessment of active demyelination and many others.

ISBN: 9781321846133Subjects--Topical Terms:

649834
Electrical engineering.

Diffusion weighted image reconstruction: A Bayesian approach.
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100 1 $a Farrah, Mustafa. $3 3188392
245 1 0 $a Diffusion weighted image reconstruction: A Bayesian approach.
300 $a 125 p.
500 $a Source: Dissertation Abstracts International, Volume: 76-11(E), Section: B.
500 $a Advisers: Chiu Law; Daniel Rowe.
502 $a Thesis (Ph.D.)--The University of Wisconsin - Milwaukee, 2015.
520 $a Diffusion weighted imaging (DWI) is a form of Magnetic Resonance Imaging (MRI) based upon measuring the random Brownian motion of water molecules within a voxel of tissue. Recently, DWI has become an important modality in the diagnostic work-up of early identification of ischemic stroke, differentiation of acute stroke from other conditions that mimic it, differentiation of epidermoid cyst from arachnoid cyst, assessment of cortical lesions in Creutzfeldt Jakob disease(CJD), assessment of active demyelination and many others.
520 $a Diffusion MRI suffers from the a low Signal to Noise Ratio (SNR) due to the presence of noise from the measurement process. Many techniques have been proposed to increase the SNR of the images. Some of these are averaging several acquisitions in order to reduce the noise variance, but these techniques require more time. Other denoising techniques that work on the DWI images are Principal Component Analysis (PCA), Non local-means algorithm, and Discrete Cosine Transform (DCT).
520 $a In this thesis we propose a Bayesian approach to denoise the DWI images. DWI images are acquired at certain b-value, which is a factor of diffusion weighted sequences. A higher b-value leads to a stronger diffusion weighting. When acquiring DWI images, we always get an image with no diffusion, known as the b0 image. This image has less noise than the other DWI images and hence can be used to improve the quality of those images. The Bayesian approach has been applied successfully in many medical applications. In this thesis, we will use the entropy between the DWI and the b0 image as a measure of similarity between the two images, i.e. lower joint entropy for closely matching images.
590 $a School code: 0263.
650 4 $a Electrical engineering. $3 649834
650 4 $a Medical imaging. $3 3172799
690 $a 0544
690 $a 0574
710 2 $a The University of Wisconsin - Milwaukee. $b Engineering. $3 2094093
773 0 $t Dissertation Abstracts International $g 76-11B(E).
790 $a 0263
791 $a Ph.D.
792 $a 2015
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3709122