Single‐shot T2 mapping using overlapping‐echo detachment planar imaging and a deep convolutional neural network. Issue 5 (24th April 2018)

• Record Type: Journal Article
• Authors: Cai, Congbo; Wang, Chao; Zeng, Yiqing; Cai, Shuhui; Liang, Dong; Wu, Yawen; Chen, Zhong; Ding, Xinghao; Zhong, Jianhui
• Is Part Of: Magnetic resonance in medicine. Volume 80:Issue 5(2018)
• Page Start: 2202
• Page End: 2214
• Publication Date: 2018-04-24
• Subjects: T2 mapping -- deep learning -- image reconstruction -- convolutional neural network -- residual network; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.27205
• Languages: English
• ISSNs: 0740-3194

Abstract

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.