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Machine Learning for Medical Image Reconstruction

First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings

  • Conference proceedings
  • © 2018

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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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 11074)

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 2018.

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Table of contents (17 papers)

  1. Front Matter

    Pages I-X
    Download chapter PDF

  2. Deep Learning for Magnetic Resonance Imaging

    1. Front Matter

      Pages 1-1
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    2. Deep Learning Super-Resolution Enables Rapid Simultaneous Morphological and Quantitative Magnetic Resonance Imaging

      • Akshay Chaudhari, Zhongnan Fang, Jin Hyung Lee, Garry Gold, Brian Hargreaves
      Pages 3-11

    3. ETER-net: End to End MR Image Reconstruction Using Recurrent Neural Network

      • Changheun Oh, Dongchan Kim, Jun-Young Chung, Yeji Han, HyunWook Park
      Pages 12-20

    4. Cardiac MR Motion Artefact Correction from K-space Using Deep Learning-Based Reconstruction

      • Ilkay Oksuz, James Clough, Aurelien Bustin, Gastao Cruz, Claudia Prieto, Rene Botnar et al.
      Pages 21-29

    5. Complex Fully Convolutional Neural Networks for MR Image Reconstruction

      • Muneer Ahmad Dedmari, Sailesh Conjeti, Santiago Estrada, Phillip Ehses, Tony Stöcker, Martin Reuter
      Pages 30-38

    6. Magnetic Resonance Fingerprinting Reconstruction via Spatiotemporal Convolutional Neural Networks

      • Fabian Balsiger, Amaresha Shridhar Konar, Shivaprasad Chikop, Vimal Chandran, Olivier Scheidegger, Sairam Geethanath et al.
      Pages 39-46

    7. Improved Time-Resolved MRA Using k-Space Deep Learning

      • Eunju Cha, Eung Yeop Kim, Jong Chul Ye
      Pages 47-54

    8. Joint Motion Estimation and Segmentation from Undersampled Cardiac MR Image

      • Chen Qin, Wenjia Bai, Jo Schlemper, Steffen E. Petersen, Stefan K. Piechnik, Stefan Neubauer et al.
      Pages 55-63

    9. Bayesian Deep Learning for Accelerated MR Image Reconstruction

      • Jo Schlemper, Daniel C. Castro, Wenjia Bai, Chen Qin, Ozan Oktay, Jinming Duan et al.
      Pages 64-71

  3. Deep Learning for Computed Tomography

    1. Front Matter

      Pages 73-73
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    2. Sparse-View CT Reconstruction Using Wasserstein GANs

      • Franz Thaler, Kerstin Hammernik, Christian Payer, Martin Urschler, Darko Štern
      Pages 75-82

    3. Detecting Anatomical Landmarks for Motion Estimation in Weight-Bearing Imaging of Knees

      • Bastian Bier, Katharina Aschoff, Christopher Syben, Mathias Unberath, Marc Levenston, Garry Gold et al.
      Pages 83-90

    4. A U-Nets Cascade for Sparse View Computed Tomography

      • Andreas Kofler, Markus Haltmeier, Christoph Kolbitsch, Marc Kachelrieß, Marc Dewey
      Pages 91-99

  4. Deep Learning for General Image Reconstruction

    1. Front Matter

      Pages 101-101
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    2. Approximate k-Space Models and Deep Learning for Fast Photoacoustic Reconstruction

      • Andreas Hauptmann, Ben Cox, Felix Lucka, Nam Huynh, Marta Betcke, Paul Beard et al.
      Pages 103-111

    3. Deep Learning Based Image Reconstruction for Diffuse Optical Tomography

      • Hanene Ben Yedder, Aïcha BenTaieb, Majid Shokoufi, Amir Zahiremami, Farid Golnaraghi, Ghassan Hamarneh
      Pages 112-119

    4. Image Reconstruction via Variational Network for Real-Time Hand-Held Sound-Speed Imaging

      • Valery Vishnevskiy, Sergio J. Sanabria, Orcun Goksel
      Pages 120-128

    5. Towards Arbitrary Noise Augmentation—Deep Learning for Sampling from Arbitrary Probability Distributions

      • Felix Horger, Tobias Würfl, Vincent Christlein, Andreas Maier
      Pages 129-137

    6. Left Atria Reconstruction from a Series of Sparse Catheter Paths Using Neural Networks

      • Alon Baram, Moshe Safran, Avi Ben-Cohen, Hayit Greenspan
      Pages 138-146
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  1. Machine Learning for Medical Image Reconstruction

Keywords

About this book

This book constitutes the refereed proceedings of the First International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018.

The 17 full papers presented were carefully reviewed and selected from 21 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

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