ArticleMagnetic resonance in medicine2019
Sparsity adaptive reconstruction for highly accelerated cardiac MRI.
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 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance of compressed-sensing cardiac accelerated cine MR in evaluating left ventricular function: a systematic review and meta-analysis.BMC medical imaging · 2026Pooled it
- MRI recovery with self-calibrated denoisers without fully-sampled data.Magma (New York, N.Y.) · 2025Article
- Accelerated real-time cine and flow under in-magnet staged exercise.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2025Article
- Clinical feasibility of two cardiac deep learning cine magnetic resonance imaging sequences: Single-breath-hold and free-breathing motion-corrected approaches.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2025Article
- Cardiac and respiratory motion extraction for MRI using pilot tone-a patient study.The international journal of cardiovascular imaging · 2024Article
- Real-time MRI motion estimation through an unsupervised k-space-driven deformable registration network (KS-RegNet).Physics in medicine and biology · 2022Article
- Ensuring respiratory phase consistency to improve cardiac function quantification in real-time CMR.Magnetic resonance in medicine · 2022Article
- AUTOMATIC EXTRACTION AND SIGN DETERMINATION OF RESPIRATORY SIGNAL IN REAL-TIME CARDIAC MAGNETIC RESONANCE IMAGING.Proceedings. IEEE International Symposium on Biomedical Imaging · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
purposeTo enable parameter-free, accelerated cardiovascular magnetic resonance (CMR).
methodsRegularized reconstruction methods, such as compressed sensing (CS), can significantly accelerate MRI data acquisition but require tuning of regularization weights. In this work, a technique, called Sparsity adaptive Composite Recovery (SCoRe) that exploits sparsity in multiple, disparate sparsifying transforms is presented. A data-driven adjustment of the relative contributions of different transforms yields a parameter-free CS recovery process. SCoRe is validated in a dynamic digital phantom as well as in retrospectively and prospectively undersampled cine CMR data.
resultsThe results from simulation and 6 retrospectively undersampled datasets indicate that SCoRe with auto-tuned regularization weights yields lower root-mean-square error (RMSE) and higher structural similarity index (SSIM) compared to state-of-the-art CS methods. In 45 prospectively undersampled datasets acquired from 15 volunteers, the image quality was scored by 2 expert reviewers, with SCoRe receiving a higher average score (p < 0.01) compared to other CS methods.
conclusionsSCoRe enables accelerated cine CMR from highly undersampled data. In contrast to other acceleration techniques, SCoRe adapts regularization weights based on noise power and level of sparsity in each transform, yielding superior performance without admitting any free parameters.
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