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

dc.contributor.authorHuang, Shuai
dc.contributor.authorLah, James J.
dc.contributor.authorAllen, Jason W.
dc.contributor.authorQiu, Deqiang
dc.contributor.departmentRadiology and Imaging Sciences, School of Medicine
dc.date.accessioned2024-12-10T09:19:08Z
dc.date.available2024-12-10T09:19:08Z
dc.date.issued2025
dc.description.abstractPurpose: 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.
dc.eprint.versionFinal published version
dc.identifier.citationHuang 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
dc.identifier.urihttps://hdl.handle.net/1805/44878
dc.language.isoen_US
dc.publisherWiley
dc.relation.isversionof10.1002/mrm.30295
dc.relation.journalMagnetic Resonance in Medicine
dc.rightsAttribution-NonCommercial 4.0 Internationalen
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0
dc.sourcePMC
dc.subjectApproximate message passing
dc.subjectCompressed sensing
dc.subjectComplementary undersampling pattern
dc.subjectHyperparameter estimation
dc.subjectMulti‐echo gradient echo sequence
dc.subjectQuantitative MRI
dc.subjectPoisson disc
dc.subjectVariable density
dc.subjectVariable flip angle
dc.titleAccelerated model‐based T1, T2* and proton density mapping using a Bayesian approach with automatic hyperparameter estimation
dc.typeArticle
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