ArticleScientific reports2024
Accelerated MRI reconstructions via variational network and feature domain learning.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Article
- Shortening MRI scanning time for acute ischemic stroke: analysis of the effect of 3.0T MRI compressed sensing deep learning reconstruction.Emergency radiology · 2026Article
- Optimized reconstruction of undersampled Dixon sequences using new memory-efficient unrolled deep neural networks: HalfVarNet and HalfDIRCN.Magnetic resonance in medicine · 2026Article
- Article
- A Hybrid Vision Transformer-BiRNN Architecture for Direct k-Space to Image Reconstruction in Accelerated MRI.Journal of imaging · 2025Article
- MR electrical properties mapping using vision transformers and canny edge detectors.Magnetic resonance in medicine · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
We introduce three architecture modifications to enhance the performance of the end-to-end (E2E) variational network (VarNet) for undersampled MRI reconstructions. We first implemented the Feature VarNet, which propagates information throughout the cascades of the network in an N-channel feature-space instead of a 2-channel feature-space. Then, we add an attention layer that utilizes the spatial locations of Cartesian undersampling artifacts to further improve performance. Lastly, we combined the Feature and E2E VarNets into the Feature-Image (FI) VarNet, to facilitate cross-domain learning and boost accuracy. Reconstructions were evaluated on the fastMRI dataset using standard metrics and clinical scoring by three neuroradiologists. Feature and FI VarNets outperformed the E2E VarNet for 4
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