Machine Learning for Medical Image Reconstruction 5th International Workshop, MLMIR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings

This book constitutes the refereed proceedings of the 5th International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2022, held in conjunction with MICCAI 2022, in September 2022, held in Singapore.

The 15 papers presented were carefully reviewed and selected from 19 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction.

Deep Learning for Magnetic Resonance Imaging.- Rethinking the optimization process for self-supervised model-driven MRI reconstruction.- NPB-REC: Non-parametric Assessment of Uncertainty in Deep-learning-based MRI Reconstruction from Undersampled Data.- Adversarial Robustness of MR Image Reconstruction under Realistic Perturbations.- High-Fidelity MRI Reconstruction with the Densely Connected Network Cascade and Feature Residual Data Consistency Priors.- Metal artifact correction MRI using multi-contrast deep neural networks for diagnosis of degenerative spinal diseases.- Segmentation-Aware MRI Reconstruction.- MRI Reconstruction with Conditional Adversarial Transformers.- Deep Learning for General Image Reconstruction- A Noise-level-aware Framework for PET Image Denoising.- DuDoTrans: Dual-Domain Transformer for Sparse-View CT Reconstruction.- Ce Wang, Kun Shang, Haimiao Zhang, Qian Li, and S. Kevin Zhou Deep Denoising Network for X-Ray Fluoroscopic Image Sequences of Moving Objects.- PP-MPI: A Deep Plug-and-Play Prior for Magnetic Particle Imaging Reconstruction.- Learning while Acquisition: Towards Active Learning Framework for Beamforming in Ultrasound Imaging.- DPDudoNet: Deep-Prior based Dual-domain Network for Low-dose Computed Tomography Reconstruction.- MTD-GAN: Multi-Task Discriminator based Generative Adversarial Networks for Low-Dose CT Denoising.- Uncertainty-Informed Bayesian PET Image Reconstruction using a Deep Image Prior.