Fidelity imposed network edit (FINE) for solving ill-posed image reconstruction

Fidelity imposed network edit (FINE) for solving ill-posed image reconstruction

Deep learning (DL) is increasingly used to solve ill-posed inverse problems in medical imaging, such as reconstruction from noisy and/or incomplete data, as DL offers advantages over conventional methods that rely on explicit image features and hand engineered priors. However, supervised DL-based me...

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Bibliographic Details
Main Authors: Jinwei Zhang, Zhe Liu, Shun Zhang, Hang Zhang, Pascal Spincemaille, Thanh D. Nguyen, Mert R. Sabuncu, Yi Wang
Format: Article
Language:English
Published: Elsevier 2020-05-01
Series:NeuroImage
Subjects:
Data fidelity
Deep learning
Inverse problem
Under-sampled image reconstruction
Quantitative susceptibility mapping
Online Access:http://www.sciencedirect.com/science/article/pii/S1053811920300665

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