Deep learning methods have been developed to estimate quantitative maps from conventional weighted images, which has the potential to improve the availability and clinical impact of quantitative MRI. However, high-resolution labels required for network training are not commonly available in practice. In this work, a hybrid supervised and physics-informed self-supervised loss function was proposed to train parameter estimation networks when only limited low-resolution labels are accessible. By taking advantage of high-resolution information from the input weighted images, the proposed method generated sharp quantitative maps and had improved performance over the supervised training method purely relying on the low-resolution labels.
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