Keywords: Cartilage, Machine Learning/Artificial Intelligence, Deep Learning, T1rho
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.
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