ArticlePhysics in medicine and biology2021
Improving subspace constrained radial fast spin echo MRI using block matching driven non-local low rank regularization.
Article in Physics in medicine and biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Methods for Uncertainty Quantification in Dictionary Matching to Advance Reliability of Quantitative MRI.Magnetic resonance in medicine · 2026Article
- Single-Shot 2D Radial Echo Planar Imaging for Functional MRI.Magnetic resonance in medicine · 2026Article
- Accelerated free-breathing abdominal T2 mapping with deep learning reconstruction of radial turbo spin-echo data.Magnetic resonance in medicine · 2025Article
- Cancellation of streak artifacts in radial abdominal imaging using interference null space projection.Magnetic resonance in medicine · 2022Article
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5 authors.
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Abstract
Subspace-constrained reconstruction methods restrict the relaxation signals (of size M) in the scene to a pre-determined subspace (of size K≪M) and allow multi-contrast imaging and parameter mapping from accelerated acquisitions. However, these constraints yield poor image quality at some imaging contrasts, which can impact the parameter mapping performance. Additional regularization such as the use of joint-sparse (JS) or locally-low-rank (LLR) constraints can help improve the recovery of these images but are not sufficient when operating at high acceleration rates. We propose a method, non-local rank 3D (NLR3D), that is built on block matching and transform domain low rank constraints to allow high quality recovery of subspace-coefficient images (SCI) and subsequent multi-contrast imaging and parameter mapping. The performance of NLR3D was evaluated using Monte-Carlo (MC) simulations and compared against the JS and LLR methods. In vivo T
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