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Machine learning for medical image reconstruction [electronic resource] : 4th International Workshop, MLMIR 2021, held in conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings / Nandinee Haq, Patricia Johnson, Andreas Maier, Tobias Würfl, Jaejun Yoo (eds.)

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...

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Bibliographic Details
Online Access: Full Text (via Springer)
Corporate Authors: MLMIR (Workshop) Online), International Conference on Medical Image Computing and Computer-Assisted Intervention
Other Authors: Haq, Nandinee, Johnson, Patricia, Maier, Andreas, Würfl, Tobias, Yoo, Jaejun
Other title:MLMIR 2021.
Format: Electronic Conference Proceeding eBook
Language:English
Published: Cham : Springer, 2021.
Series:Lecture notes in computer science ; 12964.
LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics.
Subjects:
Diagnostic imaging > Data processing > Congresses.
Artificial intelligence > Medical applications > Congresses.
Machine learning > Congresses.
Artificial intelligence > Medical applications.
Diagnostic imaging > Data processing.
Machine learning.
Conference papers and proceedings.
Table of Contents:
  • Intro
  • Preface
  • Organization
  • Contents
  • Deep Learning for Magnetic Resonance Imaging
  • HyperRecon: Regularization-Agnostic CS-MRI Reconstruction with Hypernetworks
  • 1 Introduction
  • 2 Background
  • 2.1 Amortized Optimization of CS-MRI
  • 2.2 Hypernetworks
  • 3 Proposed Method
  • 3.1 Regularization-Agnostic Reconstruction Network
  • 3.2 Training
  • 4 Experiments
  • 4.1 Hypernetwork Capacity and Hyperparameter Sampling
  • 4.2 Range of Reconstructions
  • 5 Conclusion
  • References
  • Efficient Image Registration Network for Non-Rigid Cardiac Motion Estimation
  • 1 Introduction.
  • 2 Method
  • 2.1 Network Architecture
  • 2.2 Self-supervised Loss Function
  • 2.3 Enhancement Mask (EM)
  • 3 Experiments
  • 4 Results
  • 5 Discussion
  • 6 Conclusion
  • References
  • .26em plus .1em minus .1emEvaluation of the Robustness of Learned MR Image Reconstruction to Systematic Deviations Between Training and Test Data for the Models from the fastMRI Challenge*-6pt
  • 1 Introduction
  • 2 Methods
  • 2.1 Image Perturbations
  • 2.2 Description of 2019 fastMRI Approaches
  • 3 Results
  • 4 Discussion and Conclusion
  • References
  • Self-supervised Dynamic MRI Reconstruction
  • 1 Introduction.
  • 2 Theory
  • 2.1 Dynamic MRI Reconstruction
  • 2.2 Self-supervised Learning
  • 3 Methods
  • 4 Experimental Results
  • 5 Conclusion
  • References
  • A Simulation Pipeline to Generate Realistic Breast Images for Learning DCE-MRI Reconstruction
  • 1 Introduction
  • 2 Method
  • 2.1 DCE-MRI Data Acquisition
  • 2.2 Pharmacokinetics Model Analysis and Simulation
  • 2.3 MR Acquisition Simulation
  • 2.4 Testing with ML Reconstruction
  • 3 Result
  • 4 Discussion
  • 5 Conclusion
  • References
  • Deep MRI Reconstruction with Generative Vision Transformers
  • 1 Introduction
  • 2 Theory.
  • 2.1 Deep Unsupervised MRI Reconstruction
  • 2.2 Generative Vision Transformers
  • 3 Methods
  • 4 Results
  • 5 Discussion
  • 6 Conclusion
  • References
  • Distortion Removal and Deblurring of Single-Shot DWI MRI Scans
  • 1 Introduction
  • 2 Background
  • 2.1 Distortion Removal Framework
  • 2.2 EDSR Architecture
  • 3 Distortion Removal and Deblurring of EPI-DWI
  • 3.1 Data
  • 3.2 Distortion Removal Using Structural Images
  • 3.3 Pre-processing for Super-Resolution
  • 3.4 Data Augmentation
  • 3.5 Architectures Explored for EPI-DWI Deblurring
  • 4 Experiments and Results.
  • 4.1 Computer Hardware Details
  • 4.2 Training Details
  • 4.3 Baselines
  • 4.4 Evaluation Metrics
  • 4.5 Results
  • 5 Conclusion
  • References
  • One Network to Solve Them All: A Sequential Multi-task Joint Learning Network Framework for MR Imaging Pipeline
  • 1 Introduction
  • 2 Method
  • 2.1 SampNet: The Sampling Pattern Learning Network
  • 2.2 ReconNet: The Reconstruction Network
  • 2.3 SegNet: The Segmentation Network
  • 2.4 SemuNet: The Sequential Multi-task Joint Learning Network Framework
  • 3 Experiments and Discussion
  • 3.1 Experimental Details
  • 3.2 Experiments Results.

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