Accurate estimation of multiple parameters from MRF signals using deep learning

Ideally, MRF can quantify multiple parameters at a single scan. In some cases, however, these parameters are not separable and additional scans for inseparable parameters are required, which reduces the advantage of the short scan time of MRF. This is remarkable when the large number of parameters are involved in the signal evolution process. Here, we used a pattern matching using a deep neural network called DRONE, and verified the separability of four parameters of T1, T2, B1, and ADC from MRF-FISP signals acquired at a single scan.