Machine Learning for Medical Image Reconstruction : 5th International Workshop, MLMIR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings / edited by Nandinee Haq, Patricia Johnson, Andreas Maier, Chen Qin, Tobias Würfl, Jaejun Yoo.

Format:

Book

Contributor:

Haq, Nandinee, Editor.

Johnson, Patricia, Editor.

Maier, Andreas., Editor.

Qin, Chen., Editor.

Würfl, Tobias, Editor.

Yoo, Jaejun., Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Lecture notes in computer science 1611-3349 ; 13587

Lecture Notes in Computer Science, 1611-3349 ; 13587

Language:

English

Subjects (All):

Artificial intelligence.

Image processing-Digital techniques.

Computer vision.

Computers.

Application software.

Artificial Intelligence.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Computing Milieux.

Computer and Information Systems Applications.

Local Subjects:

Artificial Intelligence.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Computing Milieux.

Computer and Information Systems Applications.

Physical Description:

1 online resource (VIII, 157 pages) : 83 illustrations, 54 illustrations in color.

Edition:

1st ed. 2022.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2022.

System Details:

text file PDF

Summary:

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.

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-031-17247-2

9783031172472

Access Restriction:

Restricted for use by site license.