Overview
- Editors:
- Nandinee Haq ORCID:https://orcid.org/0000-0002-1346-27790,
- Patricia Johnson ORCID:https://orcid.org/0000-0003-1547-99691,
- Andreas Maier ORCID:https://orcid.org/0000-0002-9550-52842,
- Chen Qin3,
- Tobias Würfl ORCID:https://orcid.org/0000-0001-9086-08964,
- …
- Jaejun Yoo ORCID:https://orcid.org/0000-0001-5252-96685
- Nandinee Haq
Hitachi, Montreal, Canada
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- Patricia Johnson
NYU Grossman School of Medicine, New York, USA
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- Andreas Maier
Friedrich-Alexander-Universität, Erlangen, Germany
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- Chen Qin
University of Edinburgh, Edinburgh, UK
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- Tobias Würfl
Siemens Healthineers, Erlangen, Germany
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- Jaejun Yoo
Ulsan National Institute of Science and Technology, Ulsan, Korea (Republic of)
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Part of the book series:Lecture Notes in Computer Science (LNCS, volume 13587)
Included in the following conference series:
Conference proceedings info: MLMIR 2022.
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About this book
The 15 papers presented were carefully reviewed and selected from 19 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction.
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Keywords
- artificial intelligence
- bioinformatics
- communication systems
- computer vision
- correlation analysis
- deep learning
- image analysis
- image processing
- image quality
- image reconstruction
- image segmentation
- machine learning
- mathematics
- neural networks
- reconstruction
- signal processing
- signal to noise ratio
- wireless telecommunication systems
Table of contents (15 papers)
Front Matter
Pages i-viiiDeep Learning for Magnetic Resonance Imaging
Front Matter
Pages 1-1Rethinking the Optimization Process for Self-supervised Model-Driven MRI Reconstruction
- Weijian Huang, Cheng Li, Wenxin Fan, Ziyao Zhang, Tong Zhang, Yongjin Zhou et al.
Pages 3-13NPB-REC: Non-parametric Assessment of Uncertainty in Deep-Learning-Based MRI Reconstruction from Undersampled Data
- Samah Khawaled, Moti Freiman
Pages 14-23Adversarial Robustness of MR Image Reconstruction Under Realistic Perturbations
- Jan Nikolas Morshuis, Sergios Gatidis, Matthias Hein, Christian F. Baumgartner
Pages 24-33High-Fidelity MRI Reconstruction with the Densely Connected Network Cascade and Feature Residual Data Consistency Priors
- Jingshuai Liu, Chen Qin, Mehrdad Yaghoobi
Pages 34-43Metal Artifact Correction MRI Using Multi-contrast Deep Neural Networks for Diagnosis of Degenerative Spinal Diseases
- Jaa-Yeon Lee, Min A Yoon, Choong Guen Chee, Jae Hwan Cho, Jin Hoon Park, Sung-Hong Park
Pages 44-52MRI Reconstruction with Conditional Adversarial Transformers
- Yilmaz Korkmaz, Muzaffer Özbey, Tolga Cukur
Pages 62-71
Deep Learning for General Image Reconstruction
Front Matter
Pages 73-73A Noise-Level-Aware Framework for PET Image Denoising
- Ye Li, Jianan Cui, Junyu Chen, Guodong Zeng, Scott Wollenweber, Floris Jansen et al.
Pages 75-83DuDoTrans: Dual-Domain Transformer for Sparse-View CT Reconstruction
- Ce Wang, Kun Shang, Haimiao Zhang, Qian Li, S. Kevin Zhou
Pages 84-94Deep Denoising Network for X-Ray Fluoroscopic Image Sequences of Moving Objects
- Wonjin Kim, Wonkyeong Lee, Sun-Young Jeon, Nayeon Kang, Geonhui Jo, Jang-Hwan Choi
Pages 95-104PP-MPI: A Deep Plug-and-Play Prior for Magnetic Particle Imaging Reconstruction
- Baris Askin, Alper Güngör, Damla Alptekin Soydan, Emine Ulku Saritas, Can Barış Top, Tolga Cukur
Pages 105-114Learning While Acquisition: Towards Active Learning Framework for Beamforming in Ultrasound Imaging
- Mayank Katare, Mahesh Raveendranatha Panicker, A. N. Madhavanunni, Gayathri Malamal
Pages 115-122DPDudoNet: Deep-Prior Based Dual-Domain Network for Low-Dose Computed Tomography Reconstruction
- Temitope Emmanuel Komolafe, Yuhang Sun, Nizhuan Wang, Kaicong Sun, Guohua Cao, Dinggang Shen
Pages 123-132MTD-GAN: Multi-task Discriminator Based Generative Adversarial Networks for Low-Dose CT Denoising
- Sunggu Kyung, JongJun Won, Seongyong Pak, Gil-sun Hong, Namkug Kim
Pages 133-144Uncertainty-Informed Bayesian PET Image Reconstruction Using a Deep Image Prior
- Viswanath P. Sudarshan, K. Pavan Kumar Reddy, Mohana Singh, Jayavardhana Gubbi, Arpan Pal
Pages 145-155
Back Matter
Pages 157-157
Other volumes
Machine Learning for Medical Image Reconstruction
Editors and Affiliations
Hitachi, Montreal, Canada
Nandinee Haq
NYU Grossman School of Medicine, New York, USA
Patricia Johnson
Friedrich-Alexander-Universität, Erlangen, Germany
Andreas Maier
University of Edinburgh, Edinburgh, UK
Chen Qin
Siemens Healthineers, Erlangen, Germany
Tobias Würfl
Ulsan National Institute of Science and Technology, Ulsan, Korea (Republic of)
Jaejun Yoo
Bibliographic Information
Book Title:Machine Learning for Medical Image Reconstruction
Book Subtitle:5th International Workshop, MLMIR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings
Editors:Nandinee Haq, Patricia Johnson, Andreas Maier, Chen Qin, Tobias Würfl, Jaejun Yoo
Series Title:Lecture Notes in Computer Science
DOI:https://doi.org/10.1007/978-3-031-17247-2
Publisher:Springer Cham
eBook Packages:Computer Science,Computer Science (R0)
Copyright Information:The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
Softcover ISBN:978-3-031-17246-5Published: 22 September 2022
eBook ISBN:978-3-031-17247-2Published: 22 September 2022
Series ISSN: 0302-9743
Series E-ISSN: 1611-3349
Edition Number:1
Number of Pages:VIII, 157
Number of Illustrations:29 b/w illustrations, 54 illustrations in colour
Topics:Artificial Intelligence,Computer Imaging, Vision, Pattern Recognition and Graphics,Computing Milieux,Computer Applications
