ArticleNeuroImage2020
Fidelity imposed network edit (FINE) for solving ill-posed image reconstruction.
Article in NeuroImage, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- UNISELF: A unified network with instance normalization and self-ensembled lesion fusion for multiple sclerosis lesion segmentation.Medical image analysis · 2026Article
- A Hybrid Vision Transformer-BiRNN Architecture for Direct k-Space to Image Reconstruction in Accelerated MRI.Journal of imaging · 2025Article
- Article
- LARO: Learned acquisition and reconstruction optimization to accelerate quantitative susceptibility mapping.NeuroImage · 2023Article
- Quantitative susceptibility mapping using multi-channel convolutional neural networks with dipole-adaptive multi-frequency inputs.Frontiers in neuroscience · 2023Article
- Article
- Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging.IEEE signal processing magazine · 2023Article
- [A multi-channel input convolutional neural network for artifact reduction in quantitative susceptibility mapping].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2022Article
- Quantitative susceptibility mapping using plug-and-play alternating direction method of multipliers.Scientific reports · 2022Article
- Unsupervised knowledge-transfer for learned image reconstruction.Inverse problems · 2022Article
- QQ-NET - using deep learning to solve quantitative susceptibility mapping and quantitative blood oxygen level dependent magnitude (QSM+qBOLD or QQ) based oxygen extraction fraction (OEF) mapping.Magnetic resonance in medicine · 2022Article
- A review and experimental evaluation of deep learning methods for MRI reconstruction.The journal of machine learning for biomedical imaging · 2022Article
- QSM reconstruction challenge 2.0: Design and report of results.Magnetic resonance in medicine · 2021Article
- Deep neural network for water/fat separation: Supervised training, unsupervised training, and no training.Magnetic resonance in medicine · 2021Article
- Deep-learning-based Optimization of the Under-sampling Pattern in MRI.IEEE transactions on computational imaging · 2020Article
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Authors and funding
8 authors.
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Abstract
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 methods may achieve poor performance when the test data deviates from the training data, for example, when it has pathologies not encountered in the training data. Furthermore, DL-based image reconstructions do not always incorporate the underlying forward physical model, which may improve performance. Therefore, in this work we introduce a novel approach, called fidelity imposed network edit (FINE), which modifies the weights of a pre-trained reconstruction network for each case in the testing dataset. This is achieved by minimizing an unsupervised fidelity loss function that is based on the forward physical model. FINE is applied to two important inverse problems in neuroimaging: quantitative susceptibility mapping (QSM) and under-sampled image reconstruction in MRI. Our experiments demonstrate that FINE can improve reconstruction accuracy.
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