SSIMPLE: Scan-SpecIfic parameter MaPping from contrast weighted images with self-supervised LEarning

Motivation: There is rich and complementary information in clinical images, which may lend itself to the estimation of relaxometry parameters. Goal(s): To develop a self-supervised network that can estimate T1, T2, and PD maps from contrast-weighted images with high fidelity. Approach: We developed a scan-specific self-supervised model (SSIMPLE) that harnesses Bloch equations and estimates parameter maps from multi-contrast images without the need for a training dataset and additional constraints. Results: High-fidelity T1, T2, and PD maps with minor biases 4.5%, 11.76%, and 15.45%, respectively, were obtained using the proposed self-supervised network. Impact: Using the developed scan-specific self-supervised neural network, SSIMPLE, high-fidelity parameter maps can be estimated from clinically routine contrast-weighted images without the need for an external training dataset or additional constraints.