Machine Learning for Medical Image Reconstruction: First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings

✍ Scribed by Florian Knoll, Andreas Maier, Daniel Rueckert

Publisher: Springer
Year: 2018
Tongue: English
Leaves: 161
Series: Lecture Notes in Computer Science
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

✦ Table of Contents

Front Matter ....Pages I-X
Front Matter ....Pages 1-1
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
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
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
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
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
Improved Time-Resolved MRA Using k-Space Deep Learning (Eunju Cha, Eung Yeop Kim, Jong Chul Ye)....Pages 47-54
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
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
Front Matter ....Pages 73-73
Sparse-View CT Reconstruction Using Wasserstein GANs (Franz Thaler, Kerstin Hammernik, Christian Payer, Martin Urschler, Darko Štern)....Pages 75-82
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
A U-Nets Cascade for Sparse View Computed Tomography (Andreas Kofler, Markus Haltmeier, Christoph Kolbitsch, Marc Kachelrieß, Marc Dewey)....Pages 91-99
Front Matter ....Pages 101-101
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
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
Image Reconstruction via Variational Network for Real-Time Hand-Held Sound-Speed Imaging (Valery Vishnevskiy, Sergio J. Sanabria, Orcun Goksel)....Pages 120-128
Towards Arbitrary Noise Augmentation—Deep Learning for Sampling from Arbitrary Probability Distributions (Felix Horger, Tobias Würfl, Vincent Christlein, Andreas Maier)....Pages 129-137
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
High Quality Ultrasonic Multi-line Transmission Through Deep Learning (Sanketh Vedula, Ortal Senouf, Grigoriy Zurakhov, Alex Bronstein, Michael Zibulevsky, Oleg Michailovich et al.)....Pages 147-155
Back Matter ....Pages 157-158

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