Machine Learning for Medical Image Reconstruction

1. Front Matter

   Pages I-X

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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

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.

• Artificial Intelligence
• Computer architecture
• Computerized tomography
• Image analysis
• Image processing
• Image quality
• Image reconstruction
• Image segmentation
• Life and medical sciences
• Machine learning
• Machine learning algorithms
• Machine learning theory
• Medical imaging
• Medical technologies
• Neural networks
• Reconstruction
• Sensors

• 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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