Christopher Man1,2, Linfang Xiao1,2, Yilong Liu1,2, Vick Lau1,2, Zheyuan Yi1,2,3, Alex T. L. Leong1,2, and Ed X. Wu1,2
1Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, China, 2Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China, 3Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China
1Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, China, 2Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China, 3Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China
A deep
learning based multi-contrast MRI reconstruction was proposed for single-channel
multi-contrast MR data undersampled uniformly and orthogonally across different
contrasts, which can effectively remove the aliasing artifacts and preserve
structure details at R=4.
Figure 2 Multi-contrast reconstruction for multi-contrast MR data at R=3, clearly demonstrating the proposed model could reconstruct
high-fidelity images without obvious
artifacts.
Figure 1 The proposed method uses a
Res-UNet architecture, which consists of 4 pooling layers. Real and imaginary
parts of complex T1- and T2-weighted images are treated as separate channels
for the model inputs.