Improving bSSFP-based quantitative magnetization transfer imaging with MR physics-informed artificial neural network

Most quantitative magnetization transfer imaging (qMT) protocols require additional T1 mapping scan. A recent on-resonance multiple phase-cycle bSSFP method was proposed for qMT that obviates the necessity for T1 mapping, but the fitting results were suboptimal. In this study, we proposed a physics-informed artificial neural network (ANN) to improve the fitting of this method. By using the MR signal model to generate the training data and regularize the network, no in-vivo data acquisition was necessary. Experiments on digital phantom and in-vivo data demonstrated improvement over previous method and better resilience against measurement noise.