Machine Learning for Medical Image Reconstruction
4th International Workshop, MLMIR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings
Published on 30. September 2021
Book
Paperback/Softback
VIII, 142 pages
978-3-030-88551-9 (ISBN)
Description
This book constitutes the refereed proceedings of the 4th International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2021, held in conjunction with MICCAI 2021, in October 2021. The workshop was planned to take place in Strasbourg, France, but was held virtually due to the COVID-19 pandemic.
The 13 papers presented were carefully reviewed and selected from 20 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction.
More details
Series
Edition
1st ed. 2021
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
37 farbige Abbildungen, 16 s/w Abbildungen
VIII, 142 p. 53 illus., 37 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 9 mm
Weight
242 gr
ISBN-13
978-3-030-88551-9 (9783030885519)
DOI
10.1007/978-3-030-88552-6
Schweitzer Classification
Other editions
Additional editions
Nandinee Haq | Patricia Johnson | Andreas Maier
Machine Learning for Medical Image Reconstruction
4th International Workshop, MLMIR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings
E-Book
09/2021
Springer
€58.84
Available for download
Content
Deep Learning for Magnetic Resonance Imaging.- HyperRecon: Regularization-Agnostic CS-MRI Reconstruction with Hypernetworks.- Efficient Image Registration Network For Non-Rigid Cardiac Motion Estimation.- Evaluation of the robustness of learned MR image reconstruction to systematic deviations between training and test data for the models from the fastMRI challenge.- Self-Supervised Dynamic MRI Reconstruction.- A Simulation Pipeline to Generate Realistic Breast Images For Learning DCE-MRI Reconstruction.- Deep MRI Reconstruction with Generative Vision Transformers.- Distortion Removal and Deblurring of Single-Shot DWI MRI Scans.- One Network to Solve Them All: A Sequential Multi-Task Joint Learning Network Framework for MR Imaging Pipeline.- Physics-informed self-supervised deep learning reconstruction for accelerated rst-pass perfusion cardiac MRI.- Deep Learning for General Image Reconstruction.- Noise2Stack: Improving Image Restoration by Learning from Volumetric Data.- Real-time Video Denoising in Fluoroscopic Imaging.- A Frequency Domain Constraint for Synthetic and Real X-ray Image Super Resolution.- Semi- and Self-Supervised Multi-View Fusion of 3D Microscopy Images using Generative Adversarial Networks.