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Physics-informed optimization of saturation-transfer MRI protocols using non-differentiable Bloch models.

Beomgu Kang1,2,3, Munendra Singh1, Hyunseok Seo2

  • 1Divison of MR Research, Department of Radiology, Johns Hopkins University, Baltimore, MD, United States of America.

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|February 5, 2026
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Summary
This summary is machine-generated.

This study introduces a learning-based framework to optimize magnetic resonance imaging (MRI) acquisition schedules for saturation transfer MR fingerprinting (ST-MRF). The optimized approach enhances quantification accuracy and efficiency for molecular imaging.

Keywords:
Bloch equationMR fingerprintingnon-differentiable modeloptimizationsaturation transfer

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Area of Science:

  • Biophysics
  • Medical Imaging
  • Machine Learning

Background:

  • Quantitative molecular MRI methods like saturation transfer MR fingerprinting (ST-MRF) are crucial for estimating tissue parameters.
  • Acquisition parameter selection significantly impacts ST-MRF quantification accuracy and efficiency.
  • Optimizing data acquisition schedules is essential for improving ST-MRF performance.

Purpose of the Study:

  • To develop a learning-based optimization framework for ST-MRF acquisition schedules.
  • To improve the efficiency and accuracy of quantitative molecular MRI.
  • To enable rapid and reliable multi-tissue parameter mapping.

Main Methods:

  • Developed a learning-based framework using a deep Bloch equation simulator as a surrogate model.
  • Incorporated physics-informed optimization for iterative updates of acquisition schedules.
  • Utilized self-supervised learning for motion artifact correction and noise suppression.

Main Results:

  • The optimized ST-MRF schedule demonstrated superior quantification accuracy compared to other schedules.
  • Accurate ∆B0 maps were estimated with minimal scans, addressing B0 inhomogeneity.
  • In vivo quantitative maps were enhanced, showing improved accuracy and reliability.

Conclusions:

  • The proposed learning-based framework optimizes ST-MRF acquisition for improved accuracy and efficiency.
  • The method effectively handles B0 and B1 inhomogeneity.
  • Optimal ST-MRF enables clinically acceptable generation of accurate multi-tissue parameter maps.