Keywords: Quantitative Imaging, Quantitative Imaging, self-supervised learning, parameter mapping
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.
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