Existing deep learning-based methods for rapid MR parametric mapping often use the reference parametric maps fitted from fully sampled images to train the networks. Nevertheless, the fitted parametric map is sensitive to the noise and the fitting algorithms. In this work, we proposed to incorporate the quantitative physical model into the deep learning framework to simultaneously reconstruct the parameter-weighted images and generate the parametric map without the reference parametric maps. Experimental results on the quantitative MR T1ρ mapping show the promising performance of the proposed framework.
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