Deep Learning MR Relaxometry with Joint Spatial-Temporal Under-Sampling

This abstract presents a deep learning method to generate MR parameter maps from very few subsampled echo images. The method uses deep convolutional neural networks to learn the nonlinear relationship between the subsampled T1rho/T2-weighted images and the T1rho/T2 maps, bypassing the conventional exponential decay models. Experimental results show that the proposed method is able to generate T1rho/T2 maps from only 2 subsampled echo images with quantitative values comparable to those of the T1rho/T2 maps generated from fully-sampled 8 echo images using the conventional exponential decay curve fitting.