Del 13587 - Lecture Notes in Computer Science

Machine Learning for Medical Image Reconstruction

5th International Workshop, MLMIR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings

Häftad, Engelska, 2022

Av Nandinee Haq, Patricia Johnson, Andreas Maier, Chen Qin, Tobias Würfl, Jaejun Yoo, Tobias Wurfl

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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.

Produktinformation

Utgivningsdatum2022-09-22
Mått155 x 235 x undefined mm
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor157
FörlagSpringer International Publishing AG
ISBN9783031172465

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Tillämpad datateknik inom Data och it
Informationsteknik: allmänt inom Data och it
Systemvetenskap och AI inom Data och it
Artificiell intelligens inom Data och it

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.