Physics-informed optimization of saturation-transfer MRI protocols using non-differentiable Bloch models
Beomgu Kang, Munendra Singh, Hyunseok Seo, HyunWook Park, Hye-Young Heo
TL;DR
This paper introduces a physics-informed optimization framework for improving the accuracy and efficiency of saturation-transfer MRI protocols.
Contribution
A novel learning-based optimization framework using a deep Bloch equation simulator to enable rapid and accurate MRI quantification.
Findings
The optimal ST-MRF schedule outperformed other acquisition schedules in quantification accuracy.
The proposed method generated accurate multi-tissue parameter maps within a clinically acceptable time.
Motion artifacts and noise were effectively suppressed using self-supervised learning techniques.
Abstract
Saturation transfer MR fingerprinting (ST-MRF) is a quantitative molecular MRI method that simultaneously estimates parameters of free water, solute, and semisolid macromolecule protons. The accuracy of these quantification is highly dependent on the choice of acquisition parameters, and thus, the optimization of the data acquisition schedule is crucial to improve acquisition efficiency and quantification accuracy. Herein, we developed a learning-based optimization framework for ST-MRF, incorporating a deep Bloch equation simulator as a surrogate model for the forward Bloch equation solver to enable rapid simulations. Notably, the deep Bloch equation simulator overcomes the non-differentiability of the original model by enabling gradient computation during backpropagation within the physics-informed optimization framework, thereby allowing iterative updates of the acquisition schedule…
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Taxonomy
TopicsLanthanide and Transition Metal Complexes · Advanced MRI Techniques and Applications · Electron Spin Resonance Studies