A Few-Shot Learning Approach for Accelerated MRI via Fusion of Data-Driven and Subject-Driven Priors
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Abstract (arXiv)
Deep neural networks (DNNs) have recently found emerging use in accelerated MRI reconstruction. DNNs typically learn data-driven priors from large datasets constituting pairs of undersampled and fully-sampled acquisitions. Acquiring such large datasets, however, might be impractical. To mitigate this limitation, we propose a few-shot learning approach for accelerated MRI that merges subject-driven priors obtained via physical signal models with data-driven priors obtained from a few training samples. Demonstrations on brain MR images from the NYU fastMRI dataset indicate that the proposed approach requires just a few samples to outperform traditional parallel imaging and DNN algorithms.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 8 of its 17 references that are in the map. First posted on arXiv 13 Mar 2021.
Builds on
8 of 17 references.
References · 8
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2018fastMRI: An Open Dataset and Benchmarks for Accelerated MRIcited by 464
- 2017MoDL: Model Based Deep Learning Architecture for Inverse Problemscited by 384
- 2017Learning a Variational Network for Reconstruction of Accelerated MRI Datacited by 340
- 2017Image reconstruction by domain transform manifold learningcited by 231
- 2017A Deep Cascade of Convolutional Neural Networks for MR Image Reconstructioncited by 74
- 2017A Transfer-Learning Approach for Accelerated MRI using Deep Neural Networkscited by 43
- 2017Deep Learning with Domain Adaptation for Accelerated Projection-Reconstruction MRcited by 31
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Citing papers per year: 2021: 1; 2022: 1.
2 papers.