ArticleMagnetic resonance in medicine2014
Compressed sensing of spatial electron paramagnetic resonance imaging.
Article in Magnetic resonance in medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 26 citations in OpenAlex.
- Partial Acquisition of Spectral Projections Accelerates Four-dimensional Spectral-spatial EPR Imaging for Mouse Tumor Models: A Feasibility Study.Molecular imaging and biology · 2024Article
- Accurate reconstruction of 4D spectral-spatial images from sparse-view data in continuous-wave EPRI.Journal of magnetic resonance (San Diego, Calif. : 1997) · 2024Article
- Directional TV algorithm for fast EPR imaging.Journal of magnetic resonance (San Diego, Calif. : 1997) · 2024Article
- 4D-image reconstruction directly from limited-angular-range data in continuous-wave electron paramagnetic resonance imaging.Journal of magnetic resonance (San Diego, Calif. : 1997) · 2023Article
- An iterative reconstruction algorithm without system matrix for EPR imaging.Journal of magnetic resonance (San Diego, Calif. : 1997) · 2022Article
- Redox-Sensitive Mapping of a Mouse Tumor Model Using Sparse Projection Sampling of Electron Paramagnetic Resonance.Antioxidants & redox signaling · 2022Article
- A balanced total-variation-Chambolle-Pock algorithm for EPR imaging.Journal of magnetic resonance (San Diego, Calif. : 1997) · 2021Article
- Algebraic reconstruction of 3D spatial EPR images from high numbers of noisy projections: An improved image reconstruction technique for high resolution fast scan EPR imaging.Journal of magnetic resonance (San Diego, Calif. : 1997) · 2020Article
- Development of a fast-scan EPR imaging system for highly accelerated free radical imaging.Magnetic resonance in medicine · 2019Article
- Optimization-based image reconstruction from sparsely sampled data in electron paramagnetic resonance imaging.Journal of magnetic resonance (San Diego, Calif. : 1997) · 2018Article
- Accelerated dynamic EPR imaging using fast acquisition and compressive recovery.Journal of magnetic resonance (San Diego, Calif. : 1997) · 2016Article
- Fast dynamic electron paramagnetic resonance (EPR) oxygen imaging using low-rank tensors.Journal of magnetic resonance (San Diego, Calif. : 1997) · 2016Article
- 3D pulse EPR imaging from sparse-view projections via constrained, total variation minimization.Journal of magnetic resonance (San Diego, Calif. : 1997) · 2015Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
purposeTo improve image quality and reduce data requirements for spatial electron paramagnetic resonance imaging (EPRI) by developing a novel reconstruction approach using compressed sensing (CS).
methodsEPRI is posed as an optimization problem, which is solved using regularized least-squares with sparsity promoting penalty terms, consisting of the l1 norms of the image itself and the total variation of the image. Pseudo-random sampling was employed to facilitate recovery of the sparse signal. The reconstruction was compared with the traditional filtered back-projection reconstruction for simulations, phantoms, isolated rat hearts, and mouse gastrointestinal (GI) tracts labeled with paramagnetic probes.
resultsA combination of pseudo-random sampling and CS was able to generate high-fidelity EPR images at high acceleration rates. For three-dimensional (3D) phantom imaging, CS-based EPRI showed little visual degradation at nine-fold acceleration. In rat heart datasets, CS-based EPRI produced high quality images with eight-fold acceleration. A high resolution mouse GI tract reconstruction demonstrated a visual improvement in spatial resolution and a doubling in signal-to-noise ratio (SNR).
conclusionA novel 3D EPRI reconstruction using compressed sensing was developed and offers superior SNR and reduced artifacts from highly undersampled data.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.