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

Abstract

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 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 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: 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 T₂ 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.

Keywords: T2 mapping; convolutional neural network; deep learning; image reconstruction; residual network.