Unified Style Transfer

1 code implementation • 20 Oct 2021 • Guanjie Huang, Hongjian He, Xiang Li, Xingchen Li, Ziang Liu

Currently, it is hard to compare and evaluate different style transfer algorithms due to chaotic definitions of style and the absence of agreed objective validation methods in the study of style transfer.

Philosophy Style Transfer +1

Model-based Synthetic Data-driven Learning (MOST-DL): Application in Single-shot T2 Mapping with Severe Head Motion Using Overlapping-echo Acquisition

1 code implementation • 30 Jul 2021 • Qinqin Yang, Yanhong Lin, Jiechao Wang, Jianfeng Bao, Xiaoyin Wang, Lingceng Ma, Zihan Zhou, Qizhi Yang, Shuhui Cai, Hongjian He, Congbo Cai, Jiyang Dong, Jingliang Cheng, Zhong Chen, Jianhui Zhong

Use of synthetic data has provided a potential solution for addressing unavailable or insufficient training samples in deep learning-based magnetic resonance imaging (MRI).

Reconstruction of Quantitative Susceptibility Maps from Phase of Susceptibility Weighted Imaging with Cross-Connected $Ψ$-Net

no code implementations • 12 Oct 2020 • Zhiyang Lu, Jun Li, Zheng Li, Hongjian He, Jun Shi

In this work, we propose to explore a new value of the high-pass filtered phase data generated in susceptibility weighted imaging (SWI), and develop an end-to-end Cross-connected $\Psi$-Net (C$\Psi$-Net) to reconstruct QSM directly from these phase data in SWI without additional pre-processing.