Accelerated model‐based T1, T2* and proton density mapping using a Bayesian approach with automatic hyperparameter estimation

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2025
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American English
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Wiley
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Abstract

Purpose: To achieve automatic hyperparameter estimation for the model-based recovery of quantitative MR maps from undersampled data, we propose a Bayesian formulation that incorporates the signal model and sparse priors among multiple image contrasts.

Theory: We introduce a novel approximate message passing framework "AMP-PE" that enables the automatic and simultaneous recovery of hyperparameters and quantitative maps.

Methods: We employed the variable-flip-angle method to acquire multi-echo measurements using gradient echo sequence. We explored undersampling schemes to incorporate complementary sampling patterns across different flip angles and echo times. We further compared AMP-PE with conventional compressed sensing approaches such as the l1 norm minimization, PICS and other model-based approaches such as GraSP, MOBA.

Results: Compared to conventional compressed sensing approaches such as the l1 -norm minimization and PICS, AMP-PE achieved superior reconstruction performance with lower errors in T*2 mapping and comparable performance in T1 and proton density mappings. When compared to other model-based approaches including GraSP and MOBA, AMP-PE exhibited greater robustness and outperformed GraSP in reconstruction error. AMP-PE offers faster speed than MOBA. AMP-PE performed better than MOBA at higher sampling rates and worse than MOBA at a lower sampling rate. Notably, AMP-PE eliminates the need for hyperparameter tuning, which is a requisite for all the other approaches.

Conclusion: AMP-PE offers the benefits of model-based recovery with the additional key advantage of automatic hyperparameter estimation. It works adeptly in situations where ground-truth is difficult to obtain and in clinical environments where it is desirable to automatically adapt hyperparameters to individual protocol, scanner and patient.

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Huang S, Lah JJ, Allen JW, Qiu D. Accelerated model-based T1, T2* and proton density mapping using a Bayesian approach with automatic hyperparameter estimation. Magn Reson Med. 2025;93(2):563-583. doi:10.1002/mrm.30295
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Magnetic Resonance in Medicine
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PMC
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