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Machine Learning for Medical Image Reconstruction

Third International Workshop, MLMIR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings

  • Conference proceedings
  • © 2020

Overview

Editors:
  1. Farah Deeba

    1. University of British Columbia, Vancouver, Canada

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  2. Patricia Johnson

    1. New York University, New York City, USA

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  3. Tobias Würfl

    1. Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany

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  4. Jong Chul Ye

    1. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12450)

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 2020.

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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. 3D FLAT: Feasible Learned Acquisition Trajectories for Accelerated MRI

      • Jonathan Alush-Aben, Linor Ackerman-Schraier, Tomer Weiss, Sanketh Vedula, Ortal Senouf, Alex Bronstein
      Pages 3-16

    3. Deep Parallel MRI Reconstruction Network Without Coil Sensitivities

      • Wanyu Bian, Yunmei Chen, Xiaojing Ye
      Pages 17-26

    4. Neural Network-Based Reconstruction in Compressed Sensing MRI Without Fully-Sampled Training Data

      • Alan Q. Wang, Adrian V. Dalca, Mert R. Sabuncu
      Pages 27-37

    5. Deep Recurrent Partial Fourier Reconstruction in Diffusion MRI

      • Fasil Gadjimuradov, Thomas Benkert, Marcel Dominik Nickel, Andreas Maier
      Pages 38-47

    6. Model-Based Learning for Quantitative Susceptibility Mapping

      • Juan Liu, Kevin M. Koch
      Pages 48-59

    7. Learning Bloch Simulations for MR Fingerprinting by Invertible Neural Networks

      • Fabian Balsiger, Alain Jungo, Olivier Scheidegger, Benjamin Marty, Mauricio Reyes
      Pages 60-69

    8. Weakly-Supervised Learning for Single-Step Quantitative Susceptibility Mapping

      • Juan Liu, Kevin M. Koch
      Pages 70-81

    9. Data-Consistency in Latent Space and Online Update Strategy to Guide GAN for Fast MRI Reconstruction

      • Shuo Chen, Shanhui Sun, Xiaoqian Huang, Dinggang Shen, Qian Wang, Shu Liao
      Pages 82-90

    10. Extending LOUPE for K-Space Under-Sampling Pattern Optimization in Multi-coil MRI

      • Jinwei Zhang, Hang Zhang, Alan Wang, Qihao Zhang, Mert Sabuncu, Pascal Spincemaille et al.
      Pages 91-101

    11. AutoSyncoder: An Adversarial AutoEncoder Framework for Multimodal MRI Synthesis

      • JayaChandra Raju, Balamurali Murugesan, Keerthi Ram, Mohanasankar Sivaprakasam
      Pages 102-110

  3. Deep Learning for General Image Reconstruction

    1. Front Matter

      Pages 111-111
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    2. A Deep Prior Approach to Magnetic Particle Imaging

      • Sören Dittmer, Tobias Kluth, Daniel Otero Baguer, Peter Maass
      Pages 113-122

    3. End-To-End Convolutional Neural Network for 3D Reconstruction of Knee Bones from Bi-planar X-Ray Images

      • Yoni Kasten, Daniel Doktofsky, Ilya Kovler
      Pages 123-133

    4. Cellular/Vascular Reconstruction Using a Deep CNN for Semantic Image Preprocessing and Explicit Segmentation

      • Leila Saadatifard, Aryan Mobiny, Pavel Govyadinov, Hien Van Nguyen, David Mayerich
      Pages 134-144

    5. Improving PET-CT Image Segmentation via Deep Multi-modality Data Augmentation

      • Kaiyi Cao, Lei Bi, Dagan Feng, Jinman Kim
      Pages 145-152

    6. Stain Style Transfer of Histopathology Images via Structure-Preserved Generative Learning

      • Hanwen Liang, Konstantinos N. Plataniotis, Xingyu Li
      Pages 153-162

  4. Back Matter

    Pages 163-163
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  1. Machine Learning for Medical Image Reconstruction

Keywords

About this book

This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshop was held virtually.

The 15 papers presented were carefully reviewed and selected from 18 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction.

Editors and Affiliations

  • University of British Columbia, Vancouver, Canada

    Farah Deeba

  • New York University, New York City, USA

    Patricia Johnson

  • Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany

    Tobias Würfl

  • Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Jong Chul Ye

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