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Machine Learning for Medical Image Reconstruction: 5th International Workshop, MLMIR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings | SpringerLink
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Machine Learning for Medical Image Reconstruction

5th International Workshop, MLMIR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings

  • Conference proceedings
  • © 2022

Overview

Editors:
  1. Nandinee Haq

    1. Hitachi, Montreal, Canada

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  2. Patricia Johnson

    1. NYU Grossman School of Medicine, New York, USA

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  3. Andreas Maier

    1. Friedrich-Alexander-Universität, Erlangen, Germany

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  4. Chen Qin

    1. University of Edinburgh, Edinburgh, UK

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  5. Tobias Würfl

    1. Siemens Healthineers, Erlangen, Germany

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  6. Jaejun Yoo

    1. 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

This book constitutes the refereed proceedings of the 5th International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2022, held in conjunction with MICCAI 2022, in September 2022, held in Singapore.

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

Table of contents (15 papers)

  1. Front Matter

    Pages i-viii
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  2. Deep Learning for Magnetic Resonance Imaging

    1. Front Matter

      Pages 1-1
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    2. Rethinking 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 3-13

    3. NPB-REC: Non-parametric Assessment of Uncertainty in Deep-Learning-Based MRI Reconstruction from Undersampled Data

      • Samah Khawaled, Moti Freiman
      Pages 14-23

    4. Adversarial Robustness of MR Image Reconstruction Under Realistic Perturbations

      • Jan Nikolas Morshuis, Sergios Gatidis, Matthias Hein, Christian F. Baumgartner
      Pages 24-33

    5. High-Fidelity MRI Reconstruction with the Densely Connected Network Cascade and Feature Residual Data Consistency Priors

      • Jingshuai Liu, Chen Qin, Mehrdad Yaghoobi
      Pages 34-43

    6. Metal 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 44-52

    7. Segmentation-Aware MRI Reconstruction

      • Mert Acar, Tolga Çukur, İlkay Öksüz
      Pages 53-61

    8. MRI Reconstruction with Conditional Adversarial Transformers

      • Yilmaz Korkmaz, Muzaffer Özbey, Tolga Cukur
      Pages 62-71

  3. Deep Learning for General Image Reconstruction

    1. Front Matter

      Pages 73-73
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    2. A Noise-Level-Aware Framework for PET Image Denoising

      • Ye Li, Jianan Cui, Junyu Chen, Guodong Zeng, Scott Wollenweber, Floris Jansen et al.
      Pages 75-83

    3. DuDoTrans: Dual-Domain Transformer for Sparse-View CT Reconstruction

      • Ce Wang, Kun Shang, Haimiao Zhang, Qian Li, S. Kevin Zhou
      Pages 84-94

    4. Deep 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 95-104

    5. PP-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 105-114

    6. Learning While Acquisition: Towards Active Learning Framework for Beamforming in Ultrasound Imaging

      • Mayank Katare, Mahesh Raveendranatha Panicker, A. N. Madhavanunni, Gayathri Malamal
      Pages 115-122

    7. DPDudoNet: 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 123-132

    8. MTD-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 133-144

    9. Uncertainty-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

  4. Back Matter

    Pages 157-157
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Other volumes

  1. 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

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