ArticleMagnetic resonance in medicine2025
Repeatability-encouraging self-supervised learning reconstruction for quantitative MRI.
Article in Magnetic resonance in medicine, 2025. 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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Who cites it
4 citing papers in PubMed.
- Spatial-temporal and physical constrained deep learning model for simultaneous T1 and T2 reconstruction and mapping (STEP).Quantitative imaging in medicine and surgery · 2026Article
- MoCo + ROVir: Synergy Between Respiratory Motion Compensation and Cardiac Receive Region Focusing for Cardiac MRI.Magnetic resonance in medicine · 2026Article
- Logarithmic Scaling of Loss Functions for Enhanced Self-Supervised Accelerated MRI Reconstruction.Diagnostics (Basel, Switzerland) · 2025Article
- Repeatability-encouraging self-supervised learning reconstruction for quantitative MRI.Magnetic resonance in medicine · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
purposeThe clinical value of quantitative MRI hinges on its measurement repeatability. Deep learning methods to reconstruct undersampled quantitative MRI can accelerate reconstruction but do not aim to promote quantitative repeatability. This study proposes a repeatability-encouraging self-supervised learning (SSL) reconstruction method for quantitative MRI.
methodsThe proposed SSL reconstruction network minimized cross-data-consistency between two equally sized, mutually exclusive temporal subsets of k-t-space data, encouraging repeatability by enabling each subset's reconstruction to predict the other's k-t-space data. The method was evaluated on cardiac MR Multitasking T
resultsOn 60-s inputs, the proposed method produced comparable-quality images and T
conclusionWithout the need for labeled training data, the proposed SSL method demonstrated superior repeatability compared with supervised learning without sacrificing sharpness, and reduced reconstruction time versus iterative methods.
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