<scp>Only‐train‐once MR</scp> fingerprinting for <scp>B<sub>0</sub></scp> and <scp>B<sub>1</sub></scp> inhomogeneity correction in quantitative magnetization‐transfer contrast
Beomgu Kang, Munendra Singh, HyunWook Park, Hye‐Young Heo
Abstract
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 B 0 and B 1 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 B 0 and B 1 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 B 0 and B 1 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 B 0 and B 1 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.