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Link to original content: https://doi.org/10.1007/978-3-030-88552-6
Machine Learning for Medical Image Reconstruction: 4th International Workshop, MLMIR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings | SpringerLink
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Machine Learning for Medical Image Reconstruction

4th International Workshop, MLMIR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings

  • Conference proceedings
  • © 2021

Overview

Editors:
  1. Nandinee Haq

    1. Sunnybrook Health Science Centre, Toronto, Canada

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    You can also search for this editor in PubMed   Google Scholar

  2. Patricia Johnson

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

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    You can also search for this editor in PubMed   Google Scholar

  3. Andreas Maier

    1. Pattern Recognition Lab at FAU, Erlangen, Germany

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  4. Tobias Würfl

    1. Siemens Healthineers, Erlangen, Germany

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  5. Jaejun Yoo

    1. Ulsan National Institute of Science and Technology, Ulsan, Korea (Republic of)

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    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12964)

Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)

Included in the following conference series:

Conference proceedings info: MLMIR 2021.

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About this book

This book constitutes the refereed proceedings of the 4th International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2021, held in conjunction with MICCAI 2021, in October 2021. The workshop was planned to take place in Strasbourg, France, but was held virtually due to the COVID-19 pandemic. 

The 13 papers presented were carefully reviewed and selected from 20 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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Table of contents (13 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. HyperRecon: Regularization-Agnostic CS-MRI Reconstruction with Hypernetworks

      • Alan Q. Wang, Adrian V. Dalca, Mert R. Sabuncu
      Pages 3-13

    3. Efficient Image Registration Network for Non-Rigid Cardiac Motion Estimation

      • Jiazhen Pan, Daniel Rueckert, Thomas Küstner, Kerstin Hammernik
      Pages 14-24

    4. Evaluation of the Robustness of Learned MR Image Reconstruction to Systematic Deviations Between Training and Test Data for the Models from the fastMRI Challenge

      • Patricia M. Johnson, Geunu Jeong, Kerstin Hammernik, Jo Schlemper, Chen Qin, Jinming Duan et al.
      Pages 25-34

    5. Self-supervised Dynamic MRI Reconstruction

      • Mert Acar, Tolga Çukur, İlkay Öksüz
      Pages 35-44

    6. A Simulation Pipeline to Generate Realistic Breast Images for Learning DCE-MRI Reconstruction

      • Zhengnan Huang, Jonghyun Bae, Patricia M. Johnson, Terlika Sood, Laura Heacock, Justin Fogarty et al.
      Pages 45-53

    7. Deep MRI Reconstruction with Generative Vision Transformers

      • Yilmaz Korkmaz, Mahmut Yurt, Salman Ul Hassan Dar, Muzaffer Özbey, Tolga Cukur
      Pages 54-64

    8. Distortion Removal and Deblurring of Single-Shot DWI MRI Scans

      • Ahana Roy Choudhury, Sachin R. Jambawalikar, Piyush Kumar, Venkat Sumanth Reddy Bommireddy
      Pages 65-75

    9. One Network to Solve Them All: A Sequential Multi-task Joint Learning Network Framework for MR Imaging Pipeline

      • Zhiwen Wang, Wenjun Xia, Zexin Lu, Yongqiang Huang, Yan Liu, Hu Chen et al.
      Pages 76-85

    10. Physics-Informed Self-supervised Deep Learning Reconstruction for Accelerated First-Pass Perfusion Cardiac MRI

      • Elena Martín-González, Ebraham Alskaf, Amedeo Chiribiri, Pablo Casaseca-de-la-Higuera, Carlos Alberola-López, Rita G. Nunes et al.
      Pages 86-95

  3. Deep Learning for General Image Reconstruction

    1. Front Matter

      Pages 97-97
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    2. Noise2Stack: Improving Image Restoration by Learning from Volumetric Data

      • Mikhail Papkov, Kenny Roberts, Lee Ann Madissoon, Jarrod Shilts, Omer Bayraktar, Dmytro Fishman et al.
      Pages 99-108

    3. Real-Time Video Denoising to Reduce Ionizing Radiation Exposure in Fluoroscopic Imaging

      • Dave Van Veen, Ben A. Duffy, Long Wang, Keshav Datta, Tao Zhang, Greg Zaharchuk et al.
      Pages 109-119

    4. A Frequency Domain Constraint for Synthetic and Real X-ray Image Super Resolution

      • Qing Ma, Jae Chul Koh, WonSook Lee
      Pages 120-129

    5. Semi- and Self-supervised Multi-view Fusion of 3D Microscopy Images Using Generative Adversarial Networks

      • Canyu Yang, Dennis Eschweiler, Johannes Stegmaier
      Pages 130-139

  4. Back Matter

    Pages 141-142
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Other volumes

  1. Machine Learning for Medical Image Reconstruction

Editors and Affiliations

  • Sunnybrook Health Science Centre, Toronto, Canada

    Nandinee Haq

  • NYU Grossman School of Medicine, New York City, USA

    Patricia Johnson

  • Pattern Recognition Lab at FAU, Erlangen, Germany

    Andreas Maier

  • 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: 4th International Workshop, MLMIR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings

  • Editors: Nandinee Haq, Patricia Johnson, Andreas Maier, Tobias Würfl, Jaejun Yoo

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-030-88552-6

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer Nature Switzerland AG 2021

  • Softcover ISBN: 978-3-030-88551-9Published: 30 September 2021

  • eBook ISBN: 978-3-030-88552-6Published: 29 September 2021

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: VIII, 142

  • Number of Illustrations: 16 b/w illustrations, 37 illustrations in colour

  • Topics: Artificial Intelligence

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