國立虎尾科技大學 |

Deep learning for biomedical image reconstruction /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Deep learning for biomedical image reconstruction // edited by Jong Chul Ye, Yonina C. Eldar, Michael Unser.
other author:	Ye, Jong Chul,
Description:	1 online resource (xxii, 341 pages) :digital, PDF file(s). :
Notes:	Title from publisher's bibliographic system (viewed on 15 Sep 2023).
Subject:	Diagnostic imaging. -
Online resource:	https://doi.org/10.1017/9781009042529
ISBN:	9781009042529 (ebook)

Deep learning for biomedical image reconstruction /
Deep learning for biomedical image reconstruction /edited by Jong Chul Ye, Yonina C. Eldar, Michael Unser. - 1 online resource (xxii, 341 pages) :digital, PDF file(s).

Title from publisher's bibliographic system (viewed on 15 Sep 2023).

Formalizing Deep Neural Networks Michael Unser Geometry of Deep Learning Jong Chul Ye, Sangmin Lee Model based Reconstruction with Learning From Unsupervised to Supervised and Beyond Saiprasad Ravishankar, Zhishen Huang, Michael McCann, Siqi Ye Deep Algorithm Unrolling for Biomedical Imaging Yuelong Li, Or Bar Shira, Vishal Monga and Yonina C. Eldar Deep Learning for CT Image Reconstruction Haimiao Zhang, Bin Dong, Ge Wang, Baodong Liu Deep learning in CT reconstruction : bring the measured data to tasks / Guang-Hong Chen, Chengzhu Zhang, Yinsheng Li, Yoseob Han, Jong Chul Ye -- Overview deep learning reconstruction of accelerated MRI / Patricia Johnson, Florian Knoll -- Model-based deep learning algorithms for inverse problems / Mathews Jacob, Hemant K. Aggarwal, and Qing Zou -- k-space deep learning for MR reconstruction and artifact removal / Mehmet Akcakaya, Gyutaek Oh, Jong Chul Ye -- Deep learning for ultrasound beamforming / Ruud JG van Sloun, Jong Chul Ye and Yonina C Eldar -- Ultrasound image artifact removal using deep neural network / Jaeyoung Huh, Shujaat Khan, Jong Chul Ye -- Deep Generative Models for Biomedical Image Reconstruction / Jaejun Yoo, Michael Unser -- Image synthesis in multi-contrast MRI with generative adversarial networks / Tolga Cukur, Mahmut Yurt, Salman Ul Hassan Dar, Hyungjin Chung, Jong Chul Ye -- Regularizing Deep-Neural-Network Paradigm for the Reconstruction of Dynamic Magnetic Resonance Images / Jaejun Yoo, Michael Unser -- Regularizing Neural Network for Phase Unwrapping / Thanh-an Pham, Fangshu Yang, Michael Unser -- CryoGAN : A Deep Generative Adversarial Approach to Single-Particle Cryo-EM / Michael T. McCann, Laur`ene Donati, Harshit Gupta, Michael Unser.

Discover the power of deep neural networks for image reconstruction with this state-of-the-art review of modern theories and applications. The background theory of deep learning is introduced step-by-step, and by incorporating modeling fundamentals this book explains how to implement deep learning in a variety of modalities, including X-ray, CT, MRI and others. Real-world examples demonstrate an interdisciplinary approach to medical image reconstruction processes, featuring numerous imaging applications. Recent clinical studies and innovative research activity in generative models and mathematical theory will inspire the reader towards new frontiers. This book is ideal for graduate students in Electrical or Biomedical Engineering or Medical Physics.

ISBN: 9781009042529 (ebook)Subjects--Topical Terms:

591439
Diagnostic imaging.

LC Class. No.: RC78.7.D53 / D44 2023

Dewey Class. No.: 616.07/54

Deep learning for biomedical image reconstruction /
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505 0 $a Formalizing Deep Neural Networks Michael Unser Geometry of Deep Learning Jong Chul Ye, Sangmin Lee Model based Reconstruction with Learning From Unsupervised to Supervised and Beyond Saiprasad Ravishankar, Zhishen Huang, Michael McCann, Siqi Ye Deep Algorithm Unrolling for Biomedical Imaging Yuelong Li, Or Bar Shira, Vishal Monga and Yonina C. Eldar Deep Learning for CT Image Reconstruction Haimiao Zhang, Bin Dong, Ge Wang, Baodong Liu Deep learning in CT reconstruction : bring the measured data to tasks / Guang-Hong Chen, Chengzhu Zhang, Yinsheng Li, Yoseob Han, Jong Chul Ye -- Overview deep learning reconstruction of accelerated MRI / Patricia Johnson, Florian Knoll -- Model-based deep learning algorithms for inverse problems / Mathews Jacob, Hemant K. Aggarwal, and Qing Zou -- k-space deep learning for MR reconstruction and artifact removal / Mehmet Akcakaya, Gyutaek Oh, Jong Chul Ye -- Deep learning for ultrasound beamforming / Ruud JG van Sloun, Jong Chul Ye and Yonina C Eldar -- Ultrasound image artifact removal using deep neural network / Jaeyoung Huh, Shujaat Khan, Jong Chul Ye -- Deep Generative Models for Biomedical Image Reconstruction / Jaejun Yoo, Michael Unser -- Image synthesis in multi-contrast MRI with generative adversarial networks / Tolga Cukur, Mahmut Yurt, Salman Ul Hassan Dar, Hyungjin Chung, Jong Chul Ye -- Regularizing Deep-Neural-Network Paradigm for the Reconstruction of Dynamic Magnetic Resonance Images / Jaejun Yoo, Michael Unser -- Regularizing Neural Network for Phase Unwrapping / Thanh-an Pham, Fangshu Yang, Michael Unser -- CryoGAN : A Deep Generative Adversarial Approach to Single-Particle Cryo-EM / Michael T. McCann, Laur`ene Donati, Harshit Gupta, Michael Unser.
520 $a Discover the power of deep neural networks for image reconstruction with this state-of-the-art review of modern theories and applications. The background theory of deep learning is introduced step-by-step, and by incorporating modeling fundamentals this book explains how to implement deep learning in a variety of modalities, including X-ray, CT, MRI and others. Real-world examples demonstrate an interdisciplinary approach to medical image reconstruction processes, featuring numerous imaging applications. Recent clinical studies and innovative research activity in generative models and mathematical theory will inspire the reader towards new frontiers. This book is ideal for graduate students in Electrical or Biomedical Engineering or Medical Physics.
650 0 $a Diagnostic imaging. $3 591439
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700 1 $a Eldar, Yonina C., $e editor. $3 1439828
700 1 $a Unser, Michael A., $e editor. $3 1439829
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856 4 0 $u https://doi.org/10.1017/9781009042529