Deformation-Compensated Learning for Image Reconstruction Without Ground Truth-Reference-Cited by-同舟云学术

Deformation-Compensated Learning for Image Reconstruction Without Ground Truth

Published:2022-09 Issue:9 Volume:41 Page:2371-2384
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Gan Weijie¹^ORCID,Sun Yu¹^ORCID,Eldeniz Cihat²^ORCID,Liu Jiaming³^ORCID,An Hongyu⁴^ORCID,Kamilov Ulugbek S.⁵^ORCID

Affiliation:

1. Department of Computer Science & Engineering, Washington University in St. Louis, St. Louis, MO, USA

2. Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA

3. Department of Electrical & System Engineering, Washington University in St. Louis, St. Louis, MO, USA

4. Department of Neurology, Department of Biomedical Engineering, Mallinckrodt Institute of Radiology, Saint Louis, MO, USA

5. Department of Computer Science & Engineering and Electrical & Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA

Funder

National Science Foundation

Washington University Institute of Clinical and Translational Sciences from the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software

Link

https://ieeexplore.ieee.org/ielam/42/9872156/9743932-aam.pdf

Reference87 articles.

1. Enhanced Deep Residual Networks for Single Image Super-Resolution

2. Adam: A method for stochastic optimization;kingma;arXiv 1412 6980,2014

3. End‐to‐end deep learning nonrigid motion‐corrected reconstruction for highly accelerated free‐breathing coronary MRA

4. Self-supervised learning-based diffeomorphic non-rigid motion estimation for fast motion-compensated coronary MR angiography

5. CAPTURE: Consistently Acquired Projections for Tuned and Robust Estimation

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Quantitative analysis of deep learning-based denoising model efficacy on optical coherence tomography images with different noise levels;Photodiagnosis and Photodynamic Therapy;2024-02

2. Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review;IEEE Transactions on Medical Imaging;2024-02

3. Self-Supervised Deep Equilibrium Models With Theoretical Guarantees and Applications to MRI Reconstruction;IEEE Transactions on Computational Imaging;2023

4. Plug-and-Play Methods for Integrating Physical and Learned Models in Computational Imaging: Theory, algorithms, and applications;IEEE Signal Processing Magazine;2023-01

5. A Theoretical Framework for Self-Supervised MR Image Reconstruction Using Sub-Sampling via Variable Density Noisier2Noise;IEEE Transactions on Computational Imaging;2023