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POCS Augmented CycleGan for MR Image Reconstruction.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	POCS Augmented CycleGan for MR Image Reconstruction./
Author:	Yang, Hanlu.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	63 p.
Notes:	Source: Masters Abstracts International, Volume: 81-12.
Contained By:	Masters Abstracts International81-12.
Subject:	Electrical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27836877
ISBN:	9798645494483

POCS Augmented CycleGan for MR Image Reconstruction.
Yang, Hanlu.

POCS Augmented CycleGan for MR Image Reconstruction. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 63 p.

Source: Masters Abstracts International, Volume: 81-12.

Thesis (M.S.E.E.)--Temple University, 2020.

This item must not be sold to any third party vendors.

Traditional Magnetic Resonance Imaging (MRI) reconstruction methods, which may be highly time-consuming and sensitive to noise, heavily depend on solving nonlinear optimization problems. By contrast, deep learning (DL)-based reconstruction methods do not need any explicit analytical data model and are robust to noise due to its large data-based training, which both make DL a versatile tool for fast and high-fidelity MR image reconstruction. While DL can be performed completely independently of traditional methods, it can, in fact, benefit from incorporating these established methods to achieve better results. To test this hypothesis, we proposed a hybrid DL-based MR image reconstruction method, which combines two state-of-the-art deep learning networks, U-Net and Generative Adversarial Network with Cycle loss (CycleGAN), with a traditional data reconstruction method: Projection Onto Convex Sets (POCS). Experiments were then performed to evaluate the method by comparing it to several existing state-of-the-art methods. Our results demonstrate that the proposed method outperformed the current state-of-the-art in terms of higher peak signal-to-noise ratio (PSNR) and higher Structural Similarity Index (SSIM).

ISBN: 9798645494483Subjects--Topical Terms:

596380
Electrical engineering.
Subjects--Index Terms:

Compressed Sensing

POCS Augmented CycleGan for MR Image Reconstruction.
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245 1 0 $a POCS Augmented CycleGan for MR Image Reconstruction.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 63 p.
500 $a Source: Masters Abstracts International, Volume: 81-12.
500 $a Advisor: Bai, Li.
502 $a Thesis (M.S.E.E.)--Temple University, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a Traditional Magnetic Resonance Imaging (MRI) reconstruction methods, which may be highly time-consuming and sensitive to noise, heavily depend on solving nonlinear optimization problems. By contrast, deep learning (DL)-based reconstruction methods do not need any explicit analytical data model and are robust to noise due to its large data-based training, which both make DL a versatile tool for fast and high-fidelity MR image reconstruction. While DL can be performed completely independently of traditional methods, it can, in fact, benefit from incorporating these established methods to achieve better results. To test this hypothesis, we proposed a hybrid DL-based MR image reconstruction method, which combines two state-of-the-art deep learning networks, U-Net and Generative Adversarial Network with Cycle loss (CycleGAN), with a traditional data reconstruction method: Projection Onto Convex Sets (POCS). Experiments were then performed to evaluate the method by comparing it to several existing state-of-the-art methods. Our results demonstrate that the proposed method outperformed the current state-of-the-art in terms of higher peak signal-to-noise ratio (PSNR) and higher Structural Similarity Index (SSIM).
590 $a School code: 0225.
650 4 $a Electrical engineering. $3 596380
650 4 $a Medical imaging. $3 1180167
653 $a Compressed Sensing
653 $a CycleGAN
653 $a Deep learning
653 $a MR Image Reconstruction
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690 $a 0574
710 2 $a Temple University. $b Electrical and Computer Engineering. $3 1335270
773 0 $t Masters Abstracts International $g 81-12.
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793 $a English
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