Accelerated model‐based T1, T2* and proton density mapping using a Bayesian approach with automatic hyperparameter estimation
dc.contributor.author | Huang, Shuai | |
dc.contributor.author | Lah, James J. | |
dc.contributor.author | Allen, Jason W. | |
dc.contributor.author | Qiu, Deqiang | |
dc.contributor.department | Radiology and Imaging Sciences, School of Medicine | |
dc.date.accessioned | 2024-12-10T09:19:08Z | |
dc.date.available | 2024-12-10T09:19:08Z | |
dc.date.issued | 2025 | |
dc.description.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. | |
dc.eprint.version | Final published version | |
dc.identifier.citation | 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 | |
dc.identifier.uri | https://hdl.handle.net/1805/44878 | |
dc.language.iso | en_US | |
dc.publisher | Wiley | |
dc.relation.isversionof | 10.1002/mrm.30295 | |
dc.relation.journal | Magnetic Resonance in Medicine | |
dc.rights | Attribution-NonCommercial 4.0 International | en |
dc.rights.uri | https://creativecommons.org/licenses/by-nc/4.0 | |
dc.source | PMC | |
dc.subject | Approximate message passing | |
dc.subject | Compressed sensing | |
dc.subject | Complementary undersampling pattern | |
dc.subject | Hyperparameter estimation | |
dc.subject | Multi‐echo gradient echo sequence | |
dc.subject | Quantitative MRI | |
dc.subject | Poisson disc | |
dc.subject | Variable density | |
dc.subject | Variable flip angle | |
dc.title | Accelerated model‐based T1, T2* and proton density mapping using a Bayesian approach with automatic hyperparameter estimation | |
dc.type | Article |