Bayesian estimation of multicomponent relaxation parameters in magnetic 
resonance fingerprinting

McGivney, D; Deshmane, A; Jiang, Y; Ma, D; Badve, C; Sloan, A; Gulani, V; Griswold, M

doi:10.1002/mrm.27017

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Bayesian estimation of multicomponent relaxation parameters in magnetic resonance fingerprinting

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https://onlinelibrary.wiley.com/doi/epdf/10.1002/mrm.27017
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Citation

McGivney, D., Deshmane, A., Jiang, Y., Ma, D., Badve, C., Sloan, A., et al. (2018). Bayesian estimation of multicomponent relaxation parameters in magnetic resonance fingerprinting. Magnetic Resonance in Medicine, 80(1), 159-170. doi:10.1002/mrm.27017.

Cite as: https://hdl.handle.net/21.11116/0000-0006-9AA6-1

Abstract

Purpose

To estimate multiple components within a single voxel in magnetic resonance fingerprinting when the number and types of tissues comprising the voxel are not known a priori.
Theory

Multiple tissue components within a single voxel are potentially separable with magnetic resonance fingerprinting as a result of differences in signal evolutions of each component. The Bayesian framework for inverse problems provides a natural and flexible setting for solving this problem when the tissue composition per voxel is unknown. Assuming that only a few entries from the dictionary contribute to a mixed signal, sparsity‐promoting priors can be placed upon the solution.
Methods

An iterative algorithm is applied to compute the maximum a posteriori estimator of the posterior probability density to determine the magnetic resonance fingerprinting dictionary entries that contribute most significantly to mixed or pure voxels.
Results

Simulation results show that the algorithm is robust in finding the component tissues of mixed voxels. Preliminary in vivo data confirm this result, and show good agreement in voxels containing pure tissue.
Conclusions

The Bayesian framework and algorithm shown provide accurate solutions for the partial‐volume problem in magnetic resonance fingerprinting. The flexibility of the method will allow further study into different priors and hyperpriors that can be applied in the model.