Multi‐parametric artificial neural network fitting of phase‐cycled balanced steady‐state free precession data

Abstract

Standard relaxation time quantification using phase-cycled balanced steady-state free precession (bSSFP), eg, motion-insensitive rapid configuration relaxometry (MIRACLE), is subject to a considerable underestimation of tissue T1 and T2 due to asymmetric intra-voxel frequency distributions. In this work, an artificial neural network (ANN) fitting approach is proposed to simultaneously extract accurate reference relaxation times (T1 , T2 ) and robust field map estimates ( , ΔB0 ) from the bSSFP profile. Whole-brain bSSFP data acquired at 3T were used for the training of a feedforward ANN with N = 12, 6, and 4 phase-cycles. The magnitude and phase of the Fourier transformed complex bSSFP frequency response served as input and the multi-parametric reference set [T1 , T2 , , ∆B0 ] as target. The ANN predicted relaxation times were validated against the target and MIRACLE. The ANN prediction of T1 and T2 for trained and untrained data agreed well with the reference, even for only four acquired phase-cycles. In contrast, relaxometry based on 4-point MIRACLE was prone to severe off-resonance-related artifacts. ANN predicted and ∆B0 maps showed the expected spatial inhomogeneity patterns in high agreement with the reference measurements for 12-point, 6-point, and 4-point bSSFP phase-cycling schemes. ANNs show promise to provide accurate brain tissue T1 and T2 values as well as reliable field map estimates. Moreover, the bSSFP acquisition can be accelerated by reducing the number of phase-cycles while still delivering robust T1 , T2 , , and ∆B0 estimates.