Only-train-once MR fingerprinting for B0 and B1 inhomogeneity correction in quantitative magnetization-transfer contrast.

In Magnetic resonance in medicine ; h5-index 66.0

PURPOSE : To develop a fast, deep-learning approach for quantitative magnetization-transfer contrast (MTC)-MR fingerprinting (MRF) that simultaneously estimates multiple tissue parameters and corrects the effects of B₀ and B₁ variations.

METHODS : An only-train-once recurrent neural network was designed to perform the fast tissue-parameter quantification for a large range of different MRF acquisition schedules. It enabled a dynamic scan-wise linear calibration of the scan parameters using the measured B₀ and B₁ maps, which allowed accurate, multiple-tissue parameter mapping. MRF images were acquired from 8 healthy volunteers at 3 T. Estimated parameter maps from the MRF images were used to synthesize the MTC reference signal (Z_ref ) through Bloch equations at multiple saturation power levels.

RESULTS : The B₀ and B₁ errors in MR fingerprints, if not corrected, would impair the tissue quantification and subsequently corrupt the synthesized MTC reference images. Bloch equation-based numerical phantom studies and synthetic MRI analysis demonstrated that the proposed approach could correctly estimate water and semisolid macromolecule parameters, even with severe B₀ and B₁ inhomogeneities.

CONCLUSION : The only-train-once deep-learning framework can improve the reconstruction accuracy of brain-tissue parameter maps and be further combined with any conventional MRF or CEST-MRF method.

Kang Beomgu, Singh Munendra, Park HyunWook, Heo Hye-Young

2023-Mar-08

B0 correction, B1 correction, MR fingerprinting, deep learning, magnetization transfer contrast