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Accelerated model-based T1, T2*and proton density mapping using a Bayesian approach with automatic hyperparameter estimation
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DOI：10.1002/mrm.30295.png)
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Abstract
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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 l(1)-norm minimization, PICS and other model-based approaches such as GraSP, MOBA. Results: Compared to conventional compressed sensing approaches such as the l(1)-norm minimization and PICS, AMP-PE achieved superior reconstruction performance with lower errors in T*(2) mapping and comparable performance in T-1 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.
Keywords:
approximate message passing
compressed sensing
complementary undersampling pattern
hyperparameter estimation
multi-echo gradient echo sequence
quantitative MRI
Poisson disc
variable density
variable flip angle
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