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Fat suppression and segmentation in ...

Bayram, Ersin.

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Fat suppression and segmentation in phase-contrast MRI flow measurements.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Fat suppression and segmentation in phase-contrast MRI flow measurements./
Author:	Bayram, Ersin.
Description:	194 p.
Notes:	Source: Dissertation Abstracts International, Volume: 64-08, Section: B, page: 3925.
Contained By:	Dissertation Abstracts International64-08B.
Subject:	Engineering, Electronics and Electrical. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3102481

Fat suppression and segmentation in phase-contrast MRI flow measurements.
Bayram, Ersin.

Fat suppression and segmentation in phase-contrast MRI flow measurements. - 194 p.

Source: Dissertation Abstracts International, Volume: 64-08, Section: B, page: 3925.

Thesis (Ph.D.)--Wake Forest University, The Bowman Gray School of Medicine, 2003.

Recent developments in magnetic resonance imaging (MRI) have enabled this imaging modality to become an extremely powerful cardiovascular imaging tool. For instance, magnetic resonance (MR) phase contrast (PC) imaging holds great promise as a non-invasive diagnostic tool for measuring the blood flow in coronary arteries. Unfortunately, artifacts in the resultant images as well as current limitations of the PC technique have limited its acceptance in the medical community as an alternative to current invasive methods.Subjects--Topical Terms:

626636
Engineering, Electronics and Electrical.

Fat suppression and segmentation in phase-contrast MRI flow measurements.
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100 1 $a Bayram, Ersin. $3 1951949
245 1 0 $a Fat suppression and segmentation in phase-contrast MRI flow measurements.
300 $a 194 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-08, Section: B, page: 3925.
500 $a Adviser: Craig A. Hamilton.
502 $a Thesis (Ph.D.)--Wake Forest University, The Bowman Gray School of Medicine, 2003.
520 $a Recent developments in magnetic resonance imaging (MRI) have enabled this imaging modality to become an extremely powerful cardiovascular imaging tool. For instance, magnetic resonance (MR) phase contrast (PC) imaging holds great promise as a non-invasive diagnostic tool for measuring the blood flow in coronary arteries. Unfortunately, artifacts in the resultant images as well as current limitations of the PC technique have limited its acceptance in the medical community as an alternative to current invasive methods.
520 $a This proposal aims to improve PC flow imaging as a diagnostic tool by addressing its problems and reducing, if not eliminating, prevalent imaging artifacts in PC flow imaging. The major problems in PC flow imaging are: blurring as a result of the cardiac motion, inaccurate segmentation of the vessel, and signal contamination from the neighboring structures. For coronary circulation, neighboring structure is fat, which surrounds the whole vasculature bed. In the first part of this dissertation, current methods of addressing fat related artifacts and problems are discussed. One of these methods, spatial-spectral (SPSP) excitation, is extremely promising in terms of fat suppression; however temporal resolution requirements limit its use in PC coronary flow imaging. As a part of the dissertation work, short duration SPSP pulses are designed and optimized for PC imaging under the tight temporal resolution requirements. The efficiency of this solution has been demonstrated via simulations, phantom measurements, and real data analysis.
520 $a The second part of this thesis looks into the problems related with vessel segmentation. Accurate vessel segmentation is crucial for the success of PC imaging, as it not only decides which pixels should be included in the flow quantification, but also provides the scaling factor (vessel area) to obtain the average flow values. A segmentation method based on the deformable templates is implemented for vessel segmentation. The beauty of the method is that it incorporates human heuristic or common sense into the segmentation process. The algorithm expects a certain shape very much like a radiologist does before looking at an image. The algorithm is applied to the segmentation of ascending aorta PC flow images, and its performance is compared with the expert manual analysis results. (Abstract shortened by UMI.)
590 $a School code: 0249.
650 4 $a Engineering, Electronics and Electrical. $3 626636
650 4 $a Engineering, Biomedical. $3 1017684
650 4 $a Health Sciences, Radiology. $3 1019076
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690 $a 0574
710 2 0 $a Wake Forest University, The Bowman Gray School of Medicine. $3 1251076
773 0 $t Dissertation Abstracts International $g 64-08B.
790 1 0 $a Hamilton, Craig A., $e advisor
790 $a 0249
791 $a Ph.D.
792 $a 2003
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3102481