Comparing Different Deep Learning (DL) Models for Joint Estimation of Proton Density and T1$$$ \rho$$$ Maps in the Knee Joint

Motivation: Estimating proton density (PD) and T1$$$\rho$$$ maps in the knee joint is time-consuming with nonlinear least squares (NLS) algorithms. Deep learning (DL) methods can do it faster. Goal(s): Find the best DL model for this task, comparing different DL models. Approach: We compared UNet, DenseNet, Encoder-Decoder, and Convolutional Neural Network (CNN). The proposed models directly transform k-space data into T1$$$\rho$$$ and PD maps, eliminating the need for traditional exponential fitting. Results: UNet and Encoder-Decoder-based models obtained the best performance, using short training and prediction times and minimal memory requirements. The proposed models are 129 times faster than the benchmark NLS method. Impact: This study compared different aspects of four DL models for joint PD and T1$$$\rho$$$ maps in the knee cartilage, indicating the most recommended models for this task.