B₁ mapping using pre-learned subspaces for quantitative brain imaging

To develop a machine learning-based method for estimation of both transmitter and receiver B₁ fields desired for correction of the B₁ inhomogeneity effects in quantitative brain imaging.

A subspace model-based machine learning method was proposed for estimation of B_1t and B_1r fields. Probabilistic subspace models were used to capture scan-dependent variations in the B₁ fields; the subspace basis and coefficient distributions were learned from pre-scanned training data. Estimation of the B₁ fields for new experimental data was achieved by solving a linear optimization problem with prior distribution constraints. We evaluated the performance of the proposed method for B₁ inhomogeneity correction in quantitative brain imaging scenarios, including T₁ and proton density (PD) mapping from variable-flip-angle spoiled gradient-echo (SPGR) data as well as neurometabolic mapping from MRSI data, using phantom, healthy subject and brain tumor patient data.

In both phantom and healthy subject data, the proposed method produced high-quality B₁ maps. B₁ correction on SPGR data using the estimated B₁ maps produced significantly improved T₁ and PD maps. In brain tumor patients, the proposed method produced more accurate and robust B₁ estimation and correction results than conventional methods. The B₁ maps were also applied to MRSI data from tumor patients and produced improved neurometabolite maps, with better separation between pathological and normal tissues.

This work presents a novel method to estimate B₁ variations using probabilistic subspace models and machine learning. The proposed method may make correction of B₁ inhomogeneity effects more robust in practical applications.