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Machine Learning for Medical Image Reconstruction

Second International Workshop, MLMIR 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings

  • Conference proceedings
  • © 2019

Overview

Editors:
  1. Florian Knoll

    1. New York University, New York, USA

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  2. Andreas Maier

    1. University of Erlangen-Nuremberg, Erlangen, Germany

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  3. Daniel Rueckert

    1. Imperial College London, London, UK

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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 11905)

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 2019.

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Table of contents (25 papers)

  1. Front Matter

    Pages i-ix
    Download chapter PDF

  2. Deep Learning for Magnetic Resonance Imaging

    1. Front Matter

      Pages 1-1
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    2. Recon-GLGAN: A Global-Local Context Based Generative Adversarial Network for MRI Reconstruction

      • Balamurali Murugesan, S. Vijaya Raghavan, Kaushik Sarveswaran, Keerthi Ram, Mohanasankar Sivaprakasam
      Pages 3-15

    3. Self-supervised Recurrent Neural Network for 4D Abdominal and In-utero MR Imaging

      • Tong Zhang, Laurence H. Jackson, Alena Uus, James R. Clough, Lisa Story, Mary A. Rutherford et al.
      Pages 16-24

    4. Fast Dynamic Perfusion and Angiography Reconstruction Using an End-to-End 3D Convolutional Neural Network

      • Sahar Yousefi, Lydiane Hirschler, Merlijn van der Plas, Mohamed S. Elmahdy, Hessam Sokooti, Matthias Van Osch et al.
      Pages 25-35

    5. APIR-Net: Autocalibrated Parallel Imaging Reconstruction Using a Neural Network

      • Chaoping Zhang, Florian Dubost, Marleen de Bruijne, Stefan Klein, Dirk H. J. Poot
      Pages 36-46

    6. Accelerated MRI Reconstruction with Dual-Domain Generative Adversarial Network

      • Guanhua Wang, Enhao Gong, Suchandrima Banerjee, John Pauly, Greg Zaharchuk
      Pages 47-57

    7. Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator

      • Hongxiang Lin, Matteo Figini, Ryutaro Tanno, Stefano B. Blumberg, Enrico Kaden, Godwin Ogbole et al.
      Pages 58-70

    8. Joint Multi-anatomy Training of a Variational Network for Reconstruction of Accelerated Magnetic Resonance Image Acquisitions

      • Patricia M. Johnson, Matthew J. Muckley, Mary Bruno, Erich Kobler, Kerstin Hammernik, Thomas Pock et al.
      Pages 71-79

    9. Modeling and Analysis Brain Development via Discriminative Dictionary Learning

      • Mingli Zhang, Yuhong Guo, Caiming Zhang, Jean-Baptiste Poline, Alan Evans
      Pages 80-88

  3. Deep Learning for Computed Tomography

    1. Front Matter

      Pages 89-89
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    2. Virtual Thin Slice: 3D Conditional GAN-based Super-Resolution for CT Slice Interval

      • Akira Kudo, Yoshiro Kitamura, Yuanzhong Li, Satoshi Iizuka, Edgar Simo-Serra
      Pages 91-100

    3. Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior

      • Yixing Huang, Alexander Preuhs, Günter Lauritsch, Michael Manhart, Xiaolin Huang, Andreas Maier
      Pages 101-112

    4. Measuring CT Reconstruction Quality with Deep Convolutional Neural Networks

      • Mayank Patwari, Ralf Gutjahr, Rainer Raupach, Andreas Maier
      Pages 113-124

    5. Deep Learning Based Metal Inpainting in the Projection Domain: Initial Results

      • Tristan M. Gottschalk, Björn W. Kreher, Holger Kunze, Andreas Maier
      Pages 125-136

  4. Deep Learning for General Image Reconstruction

    1. Front Matter

      Pages 137-137
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    2. Flexible Conditional Image Generation of Missing Data with Learned Mental Maps

      • Benjamin Hou, Athanasios Vlontzos, Amir Alansary, Daniel Rueckert, Bernhard Kainz
      Pages 139-150

    3. Spatiotemporal PET Reconstruction Using ML-EM with Learned Diffeomorphic Deformation

      • Ozan Öktem, Camille Pouchol, Olivier Verdier
      Pages 151-162

    4. Stain Style Transfer Using Transitive Adversarial Networks

      • Shaojin Cai, Yuyang Xue, Qinquan Gao, Min Du, Gang Chen, Hejun Zhang et al.
      Pages 163-172

    5. Blind Deconvolution Microscopy Using Cycle Consistent CNN with Explicit PSF Layer

      • Sungjun Lim, Jong Chul Ye
      Pages 173-180
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  1. Machine Learning for Medical Image Reconstruction

Keywords

About this book

This book constitutes the refereed proceedings of the Second International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019.

The 24 full papers presented were carefully reviewed and selected from 32 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging; deep learning for computed tomography; and deep learning for general image reconstruction.

Editors and Affiliations

  • New York University, New York, USA

    Florian Knoll

  • University of Erlangen-Nuremberg, Erlangen, Germany

    Andreas Maier

  • Imperial College London, London, UK

    Daniel Rueckert

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

    Jong Chul Ye

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