ArticleMagnetic resonance in medicine2022
A Slice-Low-Rank Plus Sparse (slice-L + S) Reconstruction Method for k-t Undersampled Multiband First-Pass Myocardial Perfusion MRI.
Article in Magnetic resonance in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 7 citations in OpenAlex.
- Spatial-temporal and physical constrained deep learning model for simultaneous T1 and T2 reconstruction and mapping (STEP).Quantitative imaging in medicine and surgery · 2026Article
- Truncated enhanced constraint for low-rank plus sparse in cardiac dynamic MRI reconstruction.Magnetic resonance letters · 2026Article
- Simultaneous multi-slice cardiac real-time MRI at 0.55T.Magnetic resonance in medicine · 2025Article
- Conditional diffusion-generated super-resolution for myocardial perfusion MRI.Frontiers in cardiovascular medicine · 2025Article
- A Slice-Low-Rank Plus Sparse (slice-L + S) Reconstruction Method for k-t Undersampled Multiband First-Pass Myocardial Perfusion MRI.Magnetic resonance in medicine · 2022Article
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Authors and funding
8 authors at 2 institutions in 1 country.
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Abstract
purposeThe synergistic use of k-t undersampling and multiband (MB) imaging has the potential to provide extended slice coverage and high spatial resolution for first-pass perfusion MRI. The low-rank plus sparse (L + S) model has shown excellent performance for accelerating single-band (SB) perfusion MRI.
methodsA MB data consistency method employing ESPIRiT maps and through-plane coil information was developed. This data consistency method was combined with the temporal L + S constraint to form the slice-L + S method. Slice-L + S was compared to SB L + S and the sequential operations of split slice-GRAPPA and SB L + S (seq-SG-L + S) using synthetic data formed from multislice SB images. Prospectively k-t undersampled MB data were also acquired and reconstructed using seq-SG-L + S and slice-L + S.
resultsUsing synthetic data with total acceleration rates of 6-12, slice-L + S outperformed SB L + S and seq-SG-L + S (N = 7 subjects) with respect to normalized RMSE and the structural similarity index (P < 0.05 for both). For the specific case with MB factor = 3 and rate 3 undersampling, or for SB imaging with rate 9 undersampling (N = 7 subjects), the normalized RMSE values were 0.037 ± 0.007, 0.042 ± 0.005, and 0.031 ± 0.004; and the structural similarity index values were 0.88 ± 0.03, 0.85 ± 0.03, and 0.89 ± 0.02 for SB L + S, seq-SG-L + S, and slice-L + S, respectively (P < 0.05 for both). For prospectively undersampled MB data, slice-L + S provided better image quality than seq-SG-L + S for rate 6 (N = 7) and rate 9 acceleration (N = 7) as scored by blinded experts.
conclusionSlice-L + S outperformed SB-L + S and seq-SG-L + S and provides 9 slice coverage of the left ventricle with a spatial resolution of 1.5 mm × 1.5 mm with good image quality.
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