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MRI Signal Models of Fat and Iron in...

Horng, Debra E.

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MRI Signal Models of Fat and Iron in the Liver.

Record Type:	Electronic resources : Monograph/item
Title/Author:	MRI Signal Models of Fat and Iron in the Liver./
Author:	Horng, Debra E.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	141 p.
Notes:	Source: Dissertation Abstracts International, Volume: 78-12(E), Section: B.
Contained By:	Dissertation Abstracts International78-12B(E).
Subject:	Medical imaging. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10599550
ISBN:	9780355064339

MRI Signal Models of Fat and Iron in the Liver.
Horng, Debra E.

MRI Signal Models of Fat and Iron in the Liver. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 141 p.

Source: Dissertation Abstracts International, Volume: 78-12(E), Section: B.

Thesis (Ph.D.)--The University of Wisconsin - Madison, 2017.

Magnetic resonance imaging has traditionally provided qualitative information about patient anatomy. However, magnetic resonance methods are sensitive to the presence of different chemicals, such as fat and iron. We will exploit this sensitivity to provide quantitative measures of fat and iron in the context of the liver. Liver fat content is related to non-alcoholic fatty liver disease, while liver iron content is related to genetic hemochromatosis and repeated blood transfusions. Fat quantification requires correction for the tissue's transverse decay; at least two methods have been proposed to model the decay rate, and we will examine these signal models with both simulated and in vivo data, in the context of in vivo liver imaging by comparison to spectroscopy methods. Iron quantification can be performed using multiple methods, among them measuring the transverse decay rate, and estimating the tissue's susceptibility through phase information. We will look at the performance of measuring the transverse decay rate with differing coil combination and parameter fitting and averaging methods, in simulation and in phantoms. Finally, we will present a novel method for part of the quantitative susceptibility estimation pipeline: the theoretical basis is proffered, followed by simulation and experimental results.

ISBN: 9780355064339Subjects--Topical Terms:

3172799
Medical imaging.

MRI Signal Models of Fat and Iron in the Liver.
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020 $a 9780355064339
035 $a (MiAaPQ)AAI10599550
035 $a AAI10599550
040 $a MiAaPQ $c MiAaPQ
100 1 $a Horng, Debra E. $3 3288053
245 1 0 $a MRI Signal Models of Fat and Iron in the Liver.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 141 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-12(E), Section: B.
500 $a Adviser: Scott B. Reeder.
502 $a Thesis (Ph.D.)--The University of Wisconsin - Madison, 2017.
520 $a Magnetic resonance imaging has traditionally provided qualitative information about patient anatomy. However, magnetic resonance methods are sensitive to the presence of different chemicals, such as fat and iron. We will exploit this sensitivity to provide quantitative measures of fat and iron in the context of the liver. Liver fat content is related to non-alcoholic fatty liver disease, while liver iron content is related to genetic hemochromatosis and repeated blood transfusions. Fat quantification requires correction for the tissue's transverse decay; at least two methods have been proposed to model the decay rate, and we will examine these signal models with both simulated and in vivo data, in the context of in vivo liver imaging by comparison to spectroscopy methods. Iron quantification can be performed using multiple methods, among them measuring the transverse decay rate, and estimating the tissue's susceptibility through phase information. We will look at the performance of measuring the transverse decay rate with differing coil combination and parameter fitting and averaging methods, in simulation and in phantoms. Finally, we will present a novel method for part of the quantitative susceptibility estimation pipeline: the theoretical basis is proffered, followed by simulation and experimental results.
590 $a School code: 0262.
650 4 $a Medical imaging. $3 3172799
650 4 $a Biomedical engineering. $3 535387
690 $a 0574
690 $a 0541
710 2 $a The University of Wisconsin - Madison. $b Medical Physics. $3 2097834
773 0 $t Dissertation Abstracts International $g 78-12B(E).
790 $a 0262
791 $a Ph.D.
792 $a 2017
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10599550