Machine Learning for Medical Image Reconstruction

4th International Workshop, MLMIR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings
1. Auflage 2021
ISBN: 978-3-030-88551-9
Verlag: Springer International Publishing

Buch, Englisch, Band 12964, 142 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 242 g

Reihe: Image Processing, Computer Vision, Pattern Recognition, and Graphics

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.

Zielgruppe

Research

Autoren/Hrsg.

Haq, Nandinee

Johnson, Patricia

Yoo, Jaejun

Maier, Andreas

Würfl, Tobias

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

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.