Multiple B-Value Model-Based Residual Network (MORN) for Accelerated High-Resolution Diffusion-Weighted Imaging-Reference-Cited by-同舟云学术

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Multiple B-Value Model-Based Residual Network (MORN) for Accelerated High-Resolution Diffusion-Weighted Imaging

Published:2022-09 Issue:9 Volume:26 Page:4575-4586
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Wang Fanwen¹^ORCID,Zhang Hui¹^ORCID,Dai Fei¹,Chen Weibo²^ORCID,Xu Shuai¹,Yang Zidong¹,Shen Dinggang³^ORCID,Wang Chengyan⁴^ORCID,Wang He⁵^ORCID

Affiliation:

1. Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China

2. Philips Healthcare, Shanghai, China

3. School of Biomedical Engineering, ShanghaiTech University, Shanghai, China

4. Human Phenome Institute, Fudan University, Shanghai, China

5. Institute of Science and Technology for Brain-Inspired Intelligence and the Human Phenome Institute, Fudan University, Shanghai, China

Funder

National Natural Science Foundation of China

National Key R&D Program of China

Natural Science Foundation of Shanghai

Shanghai Municipal Science and Technology Major Project

Shanghai Sailing Program

ZJLab

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics

Link

http://xplorestaging.ieee.org/ielx7/6221020/9882959/09839600.pdf?arnumber=9839600

Reference56 articles.

1. $$\text {M}^2\text {Net}$$: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients

2. Compact Bilinear Pooling

3. MoDL-MUSSELS: Model-Based Deep Learning for Multishot Sensitivity-Encoded Diffusion MRI

4. Multi-shot sensitivity-encoded diffusion data recovery using structured low-rank matrix completion (MUSSELS)

5. SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI

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