ArticleMagnetic resonance in medicine2025
Saturation transfer MR fingerprinting for magnetization transfer contrast and chemical exchange saturation transfer quantification.
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 10 papers.
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Who cites it
10 citing papers in PubMed.
- Self-Derived Dynamic Field Map Estimation and Correction in CEST MRI.Magnetic resonance in medicine · 2026Article
- An Interpretable Deep-Learning Approach for Efficient CEST Parameter Quantification: Importance-Ranked Saturation Transfer MRI Protocol.Magnetic resonance in medicine · 2026Article
- Multi-molecular saturation-transfer MR fingerprinting for differentiating tumor progression from treatment-related change in post-treatment IDH-wildtype glioblastoma: a feasibility study.Journal of neuro-oncology · 2026Article
- Deep-learning saturation transfer magnetic resonance fingerprinting (ST-MRF) in patients with Parkinson's disease.NeuroImage · 2026Article
- Optimization of Pulsed Saturation Transfer MR Fingerprinting (ST MRF) Acquisition Using the Cramér-Rao Bound and Sequential Quadratic Programming.Magnetic resonance in medicine · 2026Article
- Physics-informed optimization of saturation-transfer MRI protocols using non-differentiable Bloch models.Physics in medicine and biology · 2026Article
- Multiparametric Saturation Transfer MR Fingerprinting Using Rosette-Accelerated Readout.NMR in biomedicine · 2026Article
- Quantitative multi-metabolite imaging of Parkinson's disease using AI boosted molecular MRI.Npj imaging · 2025Article
- Multi-contrast generation and quantitative MRI using a transformer-based framework with RF excitation embeddings.Communications biology · 2025Article
- Saturation transfer MR fingerprinting for magnetization transfer contrast and chemical exchange saturation transfer quantification.Magnetic resonance in medicine · 2025Article
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6 authors.
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Abstract
purposeThe aim of this study was to develop a saturation transfer MR fingerprinting (ST-MRF) technique using a biophysics model-driven deep learning approach.
methodsA deep learning-based quantitative saturation transfer framework was proposed to estimate water, magnetization transfer contrast, and amide proton transfer (APT) parameters plus B
resultsThe proposed ST-MRF reconstruction network achieved a normalized root mean square error (nRMSE) of 9.3% when tested against numerical phantoms with a signal-to-noise ratio of 46 dB, which outperformed conventional Bloch-McConnell fitting (nRMSE of 15.3%) and dictionary-matching approaches (nRMSE of 19.5%). Synthetic MRI analysis indicated excellent similarity (RMSE = 3.2%) between acquired and synthesized ST-MRF images, demonstrating high in vivo reconstruction accuracy. In healthy human brains, the APT pool size ratios for gray and white matter were 0.16 ± 0.02% and 0.13 ± 0.02%, respectively, and the exchange rates for gray and white matter were 101 ± 25 Hz and 131 ± 27 Hz, respectively. The reconstruction network processed the eight tissue parameter maps in approximately 27 s for ST-MRF data sized at 256 × 256 × 9 × 103.
conclusionThis study highlights the feasibility of the deep learning-based ST-MRF imaging for rapid and accurate quantification of free bulk water, magnetization transfer contrast, APT parameters, and B
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