Deep learning for biomedical image reconstruction / (Record no. 84222)

000 -LEADER
fixed length control field 03749nam a2200361 i 4500
001 - CONTROL NUMBER
control field CR9781009042529
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240730160801.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 210202s2023||||enk o ||1 0|eng|d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9781009042529 (ebook)
082 00 - CLASSIFICATION NUMBER
Call Number 616.07/54
245 00 - TITLE STATEMENT
Title Deep learning for biomedical image reconstruction /
300 ## - PHYSICAL DESCRIPTION
Number of Pages 1 online resource (xxii, 341 pages) :
500 ## - GENERAL NOTE
Remark 1 Title from publisher's bibliographic system (viewed on 15 Sep 2023).
505 0# - FORMATTED CONTENTS NOTE
Remark 2 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 ## - SUMMARY, ETC.
Summary, etc 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.
700 1# - AUTHOR 2
Author 2 Ye, Jong Chul,
700 1# - AUTHOR 2
Author 2 Eldar, Yonina C.,
700 1# - AUTHOR 2
Author 2 Unser, Michael A.,
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1017/9781009042529
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Cambridge :
-- Cambridge University Press,
-- 2023.
336 ## -
-- text
-- txt
-- rdacontent
337 ## -
-- computer
-- c
-- rdamedia
338 ## -
-- online resource
-- cr
-- rdacarrier
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Diagnostic imaging.

No items available.