ArticleMagnetic resonance in medicine2025
Generalizable, sequence-invariant deep learning image reconstruction for subspace-constrained 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 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- MoCo + ROVir: Synergy Between Respiratory Motion Compensation and Cardiac Receive Region Focusing for Cardiac MRI.Magnetic resonance in medicine · 2026Article
- All-in-One Cardiac MRI: Unified Non-Contrast-enhanced and Contrast-enhanced Imaging for a Continuous-Acquisition Examination.Radiology. Cardiothoracic imaging · 2025Article
- Generalizable, sequence-invariant deep learning image reconstruction for subspace-constrained quantitative MRI.Magnetic resonance in medicine · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
purposeTo develop a deep subspace learning network that can function across different pulse sequences.
methodsA contrast-invariant component-by-component (CBC) network structure was developed and compared against previously reported spatiotemporal multicomponent (MC) structure for reconstructing MR Multitasking images. A total of 130, 167, and 16 subjects were imaged using T
resultsThe proposed CBC showed significantly better normalized root mean squared error than MC in both matched-sequence and unmatched-sequence experiments (p < 0.001), fewer structural details in quantitative error maps, and tighter limits of agreement. CBC was more generalizable than MC (smaller performance loss; p = 0.006 in T
conclusionContrast-invariant learning of spatial features rather than spatiotemporal features improves performance and generalizability, addresses data scarcity, and offers a pathway to universal supervised deep subspace learning.
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Registered trials
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