ArticleIEEE transactions on medical imaging2020
High-Resolution Oscillating Steady-State fMRI Using Patch-Tensor Low-Rank Reconstruction.
Article in IEEE transactions on medical imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Smooth optimization using global and local low-rank regularizers.SIAM journal on imaging sciences · 2026Article
- Spatiotemporal Maps for Dynamic MRI Reconstruction.IEEE transactions on computational imaging · 2026Article
- Steady-state free precession for TMagnetic resonance in medicine · 2025Article
- Manifold Regularizer for High-Resolution fMRI Joint Reconstruction and Dynamic Quantification.IEEE transactions on medical imaging · 2024Article
- Tensor Methods in Biomedical Image Analysis.Journal of medical signals and sensors · 2024Review
- Stochastic optimization of three-dimensional non-Cartesian sampling trajectory.Magnetic resonance in medicine · 2023Article
- Enhanced clinical task-based fMRI metrics through locally low-rank denoising of complex-valued data.The neuroradiology journal · 2023Article
- High-efficiency 3D black-blood thoracic aorta imaging with patch-based low-rank tensor reconstruction.Quantitative imaging in medicine and surgery · 2023Article
- An untrained deep learning method for reconstructing dynamic MR images from accelerated model-based data.Magnetic resonance in medicine · 2023Article
- OSCILLATE: A low-rank approach for accelerated magnetic resonance elastography.Magnetic resonance in medicine · 2022Article
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Authors and funding
3 authors.
Funding
Abstract
The goals of fMRI acquisition include high spatial and temporal resolutions with a high signal to noise ratio (SNR). Oscillating Steady-State Imaging (OSSI) is a new fMRI acquisition method that provides large oscillating signals with the potential for high SNR, but does so at the expense of spatial and temporal resolutions. The unique oscillation pattern of OSSI images makes it well suited for high-dimensional modeling. We propose a patch-tensor low-rank model to exploit the local spatial-temporal low-rankness of OSSI images. We also develop a practical sparse sampling scheme with improved sampling incoherence for OSSI. With an alternating direction method of multipliers (ADMM) based algorithm, we improve OSSI spatial and temporal resolutions with a factor of 12 acquisition acceleration and 1.3 mm isotropic spatial resolution in prospectively undersampled experiments. The proposed model yields high temporal SNR with more activation than other low-rank methods. Compared to the standard grad- ient echo (GRE) imaging with the same spatial-temporal resolution, 3D OSSI tensor model reconstruction demonstrates 2 times higher temporal SNR with 2 times more functional activation.
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