This abstract presents a deep learning method to generate T1rho and T2 relaxation maps simultaneously within one scan. The method uses 3D deep convolutional neural networks to exploit the nonlinear relationship between and within the combined subsampled T1rho and T2-weighted images and the combined T1rho and T2 maps, bypassing conventional fitting models. Compare with separated trained relaxation maps, this new method also exploits the autocorrelation and cross-correlation between subsampled echoes. Experiments show that the proposed method is capable of generating T1rho and T2 maps simultaneously from only 3 subsampled echoes within one scan with quantification results comparable to reference maps.
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