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Inverse Problem in Quantitative Susc...

Liu, Zhe.

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Inverse Problem in Quantitative Susceptibility Mapping: Numerical and Machine Learning Approaches.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Inverse Problem in Quantitative Susceptibility Mapping: Numerical and Machine Learning Approaches./
Author:	Liu, Zhe.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	90 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-12, Section: B.
Contained By:	Dissertations Abstracts International80-12B.
Subject:	Mathematics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13428363
ISBN:	9781392248188

Inverse Problem in Quantitative Susceptibility Mapping: Numerical and Machine Learning Approaches.
Liu, Zhe.

Inverse Problem in Quantitative Susceptibility Mapping: Numerical and Machine Learning Approaches. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 90 p.

Source: Dissertations Abstracts International, Volume: 80-12, Section: B.

Thesis (Ph.D.)--Cornell University, 2019.

This item must not be added to any third party search indexes.

Magnetic susceptibility reflects the concentration of bio-metal elements such as iron, calcium or gadolinium, providing means to investigate diseases such as multiple sclerosis, Alzheimer's disease, hemorrhage and calcification. Numerous approaches have been proposed to provide magnetic susceptibility estimation from magnetic resonance imaging (MRI). While those methods are designed for specific body parts or pathologies, a unified framework is elusive from literature for reliable susceptibility estimation in both normal and pathological scenarios.This thesis developed algorithms that improve the accuracy, robustness and applicability of quantitative susceptibility mapping (QSM) for both healthy and pathological subjects. First, a dedicated regularized model was proposed to enable automated zero reference for QSM using cerebrospinal fluid. Second, convolutional neural network was combined with numerical optimization for superior anatomical contrast in QSM reconstruction. Finally, a total field inversion approach was presented to enable QSM for both healthy subject and hemorrhage patient. With the technical advances in this thesis, QSM requires less manual effort in susceptibility quantification, admits detailed recovery of anatomical structures and applies to both healthy subject and patient via a unified framework.

ISBN: 9781392248188Subjects--Topical Terms:

515831
Mathematics.

Inverse Problem in Quantitative Susceptibility Mapping: Numerical and Machine Learning Approaches.
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245 1 0 $a Inverse Problem in Quantitative Susceptibility Mapping: Numerical and Machine Learning Approaches.
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300 $a 90 p.
500 $a Source: Dissertations Abstracts International, Volume: 80-12, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Wang, Yi.
502 $a Thesis (Ph.D.)--Cornell University, 2019.
506 $a This item must not be added to any third party search indexes.
506 $a This item must not be sold to any third party vendors.
520 $a Magnetic susceptibility reflects the concentration of bio-metal elements such as iron, calcium or gadolinium, providing means to investigate diseases such as multiple sclerosis, Alzheimer's disease, hemorrhage and calcification. Numerous approaches have been proposed to provide magnetic susceptibility estimation from magnetic resonance imaging (MRI). While those methods are designed for specific body parts or pathologies, a unified framework is elusive from literature for reliable susceptibility estimation in both normal and pathological scenarios.This thesis developed algorithms that improve the accuracy, robustness and applicability of quantitative susceptibility mapping (QSM) for both healthy and pathological subjects. First, a dedicated regularized model was proposed to enable automated zero reference for QSM using cerebrospinal fluid. Second, convolutional neural network was combined with numerical optimization for superior anatomical contrast in QSM reconstruction. Finally, a total field inversion approach was presented to enable QSM for both healthy subject and hemorrhage patient. With the technical advances in this thesis, QSM requires less manual effort in susceptibility quantification, admits detailed recovery of anatomical structures and applies to both healthy subject and patient via a unified framework.
590 $a School code: 0058.
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650 4 $a Medical imaging. $3 3172799
650 4 $a Artificial intelligence. $3 516317
650 4 $a Computer science. $3 523869
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773 0 $t Dissertations Abstracts International $g 80-12B.
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