SGDNet: Synthetic Data Guided Supervised Deep Learning Network for Multi-Component T1$$$\rho$$$ Mapping in the Knee Joint

Motivation: The nonlinear least squares (NLS)-based estimation of multi-component T1$$$\rho$$$ maps in the knee joint is highly time-consuming. Goal(s): The main goal is to propose Synthetic Data-Guided Supervised Deep Learning Network (SGDNet), which uses synthetic target data for training, eliminating the need for extensive reference data. Approach: A customized loss function is designed to ensure consistency between actual and predicted T1$$$\rho$$$ values and between the measured and predicted MR data. A self-attention module is also integrated into SGDNet. Results: SGDNet produces T1$$$\rho$$$ maps similar to those obtained by NLS and regularized NLS (RNLS) while being, on average, 66 times faster in fitting time. Impact: Our results indicate that SGDNet is faster for whole knee joint T1$$$\rho$$$ mapping than the NLS-based methods, with comparable errors. Thus, SGDNets is an alternative to replace NLS and RNLS for T1$$$\rho$$$ mapping when computational time is an issue.