Single‐shot T₂ mapping using overlapping‐echo detachment planar imaging and a deep convolutional neural network

Purpose An end‐to‐end deep convolutional neural network (CNN) based on deep residual network (ResNet) was proposed to efficiently reconstruct reliable T 2 mapping from single‐shot overlapping‐echo detachment (OLED) planar imaging. Methods The training dataset was obtained from simulations that were carried out on SPROM (Simulation with PRoduct Operator Matrix) software developed by our group. The relationship between the original OLED image containing two echo signals and the corresponding T 2 mapping was learned by ResNet training. After the ResNet was trained, it was applied to reconstruct the T 2 mapping from simulation and in vivo human brain data. Results Although 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 outperforms the echo‐detachment‐based method. Reliable T 2 mapping with higher accuracy is achieved within 30 ms after the network has been trained, while the echo‐detachment‐based OLED reconstruction method took approximately 2 min. Conclusion The proposed method will facilitate real‐time dynamic and quantitative MR imaging via OLED sequence, and deep convolutional neural network has the potential to reconstruct maps from complex MRI sequences efficiently.