  Purpose: An end-to-end deep convolutional neural network (CNN) based on deep
residual network (ResNet) was proposed to efficiently reconstruct reliable T2
mapping from single-shot OverLapping-Echo Detachment (OLED) planar imaging.
Methods: The training dataset was obtained from simulations carried out on
SPROM software developed by our group. The relationship between the original
OLED image containing two echo signals and the corresponded T2 mapping was
learned by ResNet training. After the ResNet was trained, it was applied to
reconstruct the T2 mapping from simulation and in vivo human brain data.
Results: Though the ResNet was trained entirely on simulated data, the trained
network was generalized well to real human brain data. The results from
simulation and in vivo human brain experiments show that the proposed method
significantly outperformed the echo-detachment-based method. Reliable T2
mapping was achieved within tens of milliseconds after the network had been
trained while the echo-detachment-based OLED reconstruction method took
minutes. Conclusion: The proposed method will greatly facilitate real-time
dynamic and quantitative MR imaging via OLED sequence, and ResNet has the
potential to reconstruct images from complex MRI sequence efficiently.
