ArticleScientific reports2021
Fast data-driven learning of parallel MRI sampling patterns for large scale problems.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Jointly Learned 3D Non-Cartesian Sampling With Wave Encoding and Reconstruction for Neurovascular Phase Contrast MRI.Magnetic resonance in medicine · 2026Article
- Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO).IEEE transactions on computational imaging · 2026Article
- Optimized MR pulse sequence for high-resolution brain 3D-T1ρ mapping with weighted spin-lock acquisitions.Magnetic resonance in medicine · 2025Article
- Performance of MR learned pulse sequences for 3D bi-exponential, stretched-exponential, and mono-exponential TMagnetic resonance in medicine · 2025Article
- AutoSamp: Autoencoding k-Space Sampling via Variational Information Maximization for 3D MRI.IEEE transactions on medical imaging · 2025Article
- HDNLS: Hybrid Deep-Learning and Non-Linear Least Squares-Based Method for Fast Multi-Component T1ρ Mapping in the Knee Joint.Bioengineering (Basel, Switzerland) · 2024Article
- Artificial intelligence for neuro MRI acquisition: a review.Magma (New York, N.Y.) · 2024Review
- Emerging Trends in Magnetic Resonance Fingerprinting for Quantitative Biomedical Imaging Applications: A Review.Bioengineering (Basel, Switzerland) · 2024Review
- Optimizing variable flip angles in magnetization-prepared gradient-echo sequences for efficient 3D-T1ρ mapping.Magnetic resonance in medicine · 2023Article
- Stochastic optimization of three-dimensional non-Cartesian sampling trajectory.Magnetic resonance in medicine · 2023Article
- Efficient Approximation of Jacobian Matrices Involving a Non-Uniform Fast Fourier Transform (NUFFT).IEEE transactions on computational imaging · 2023Article
- IntelliScan: Improving the quality of x-ray computed tomography surface data through intelligent selection of projection angles.Journal of X-ray science and technology · 2023Article
- Data-driven optimization of sampling patterns for MR brain TMagnetic resonance in medicine · 2023Article
- Joint Optimization of k-t Sampling Pattern and Reconstruction of DCE MRI for Pharmacokinetic Parameter Estimation.IEEE transactions on medical imaging · 2022Article
- B-Spline Parameterized Joint Optimization of Reconstruction and K-Space Trajectories (BJORK) for Accelerated 2D MRI.IEEE transactions on medical imaging · 2022Article
- Optimization of spin-lock times in TMagnetic resonance in medicine · 2022Article
- Alternating Learning Approach for Variational Networks and Undersampling Pattern in Parallel MRI Applications.IEEE transactions on computational imaging · 2022Article
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3 authors.
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Abstract
In this study, a fast data-driven optimization approach, named bias-accelerated subset selection (BASS), is proposed for learning efficacious sampling patterns (SPs) with the purpose of reducing scan time in large-dimensional parallel MRI. BASS is applicable when Cartesian fully-sampled k-space measurements of specific anatomy are available for training and the reconstruction method for undersampled measurements is specified; such information is used to define the efficacy of any SP for recovering the values at the non-sampled k-space points. BASS produces a sequence of SPs with the aim of finding one of a specified size with (near) optimal efficacy. BASS was tested with five reconstruction methods for parallel MRI based on low-rankness and sparsity that allow a free choice of the SP. Three datasets were used for testing, two of high-resolution brain images ([Formula: see text]-weighted images and, respectively, [Formula: see text]-weighted images) and another of knee images for quantitative mapping of the cartilage. The proposed approach has low computational cost and fast convergence; in the tested cases it obtained SPs up to 50 times faster than the currently best greedy approach. Reconstruction quality increased by up to 45% over that provided by variable density and Poisson disk SPs, for the same scan time. Optionally, the scan time can be nearly halved without loss of reconstruction quality. Quantitative MRI and prospective accelerated MRI results show improvements. Compared with greedy approaches, BASS rapidly learns effective SPs for various reconstruction methods, using larger SPs and larger datasets; enabling better selection of sampling-reconstruction pairs for specific MRI problems.
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