ArticleMagnetic resonance in medicine2019
Sparsity and locally low rank regularization for MR fingerprinting.
Article in Magnetic resonance in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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47 citing papers in PubMed.
- Fully 3D Unrolled Magnetic Resonance Fingerprinting Reconstruction via Staged Pretraining and Implicit Gridding.Magnetic resonance in medicine · 2026Article
- Effect of basis choice on quantitative parameter estimation in accelerated subspace reconstructions.Magnetic resonance letters · 2026Article
- A Magnetic Resonance Fingerprinting Approach for SimultaneousMagnetic resonance in medicine · 2026Article
- 3D MR Fingerprinting for Quantitative Bladder Wall TNMR in biomedicine · 2026Article
- Bilevel Optimized Implicit Neural Representation for Scan-Specific Accelerated MRI Reconstruction.IEEE transactions on medical imaging · 2026Article
- Cardiac MR Fingerprinting at 0.55T Using a Deep Image Prior for Joint TJournal of magnetic resonance imaging : JMRI · 2026Article
- Simultaneous TMagnetic resonance in medicine · 2025Article
- Distortion-free steady-state diffusion-weighted imaging with magnetic resonance fingerprinting.Medical physics · 2025Article
- Three-dimensional high-isotropic-resolution MR fingerprinting optimized for 0.55 T.Magnetic resonance in medicine · 2025Article
- SuperMRF: deep robust reconstruction for highly accelerated magnetic resonance fingerprinting.Quantitative imaging in medicine and surgery · 2025Article
- Feasibility of 3D MRI fingerprinting for rapid knee cartilage TNMR in biomedicine · 2024Article
- Efficient pulse sequence design framework for high-dimensional MR fingerprinting scans using systematic error index.Magnetic resonance in medicine · 2024Article
- Accelerated Cartesian cardiac T2 mapping based on a calibrationless locally low-rank tensor constraint.Quantitative imaging in medicine and surgery · 2024Article
- Fast Whole-Brain MR Multi-Parametric Mapping with Scan-Specific Self-Supervised Networks.ArXiv · 2024Article
- High-fidelity intravoxel incoherent motion parameter mapping using locally low-rank and subspace modeling.NeuroImage · 2024Article
- Simultaneous multislice cardiac multimapping based on locally low-rank and sparsity constraints.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2024Article
- Synthetic multi-contrast late gadolinium enhancement imaging using post-contrast magnetic resonance fingerprinting.NMR in biomedicine · 2024Article
- MR Fingerprinting for Contrast Agent-free and Quantitative Characterization of Focal Liver Lesions.Radiology. Imaging cancer · 2023Article
- Global deep learning optimization of chemical exchange saturation transfer magnetic resonance fingerprinting acquisition schedule.NMR in biomedicine · 2023Article
- Locally Low-Rank Denoising of Multi-Echo Functional MRI Data With Application in Resting-State Analysis.Topics in magnetic resonance imaging : TMRI · 2023Article
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Authors and funding
6 authors.
Funding
Abstract
purposeDevelop a sparse and locally low rank (LLR) regularized reconstruction to accelerate MR fingerprinting (MRF).
methodsRecent works have introduced low rank reconstructions to MRF, based on temporal compression operators learned from the MRF dictionary. In other MR applications, LLR regularization has been introduced to exploit temporal redundancy in local regions of the image. Here, we propose to include spatial sparsity and LLR regularization terms in the MRF reconstruction. This approach, so called SLLR-MRF, further reduces aliasing in the time-point images and enables higher acceleration factors. The proposed approach was evaluated in simulations, T
resultsSimulations, phantom, and in vivo results show that low rank MRF reconstructions with high acceleration factors (<875 time-point images, 1 radial spoke per time-point) have residual aliasing artifacts that propagate into the parametric maps. The artifacts are reduced with the proposed SLLR-MRF resulting in considerable improvements in precision, without changes in accuracy. In vivo results show improved parametric maps for the proposed SLLR-MRF, potentially enabling MRF acquisitions with 1 radial spoke per time-point in approximately 2.6 s (~600 time-point images) for 2 × 2 mm and 9.6 s (1750 time-point images) for 1 × 1 mm in-plane resolution.
conclusionThe proposed SLLR-MRF reconstruction further improves parametric map quality compared with low rank MRF, enabling shorter scan times and/or increased spatial resolution.
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