FlexDTI: Flexible diffusion gradient encoding scheme-based highly efficient diffusion tensor imaging using deep learning

no code implementations • 2 Aug 2023 • Zejun Wu, Jiechao Wang, Zunquan Chen, Qinqin Yang, Zhen Xing, Dairong Cao, Jianfeng Bao, Taishan Kang, Jianzhong Lin, Shuhui Cai, Zhong Chen, Congbo Cai

Significance: FlexDTI can well learn diffusion gradient direction information to achieve generalized DTI reconstruction with flexible diffusion gradient scheme.

High-efficient Bloch simulation of magnetic resonance imaging sequences based on deep learning

no code implementations • 19 Oct 2022 • Haitao Huang, Qinqin Yang, Jiechao Wang, Pujie Zhang, Shuhui Cai, Congbo Cai

Significance: As a proof-of-concept work, Simu-Net shows the potential to apply deep learning for rapidly approximating the forward physical process of MRI and may increase the efficiency of Bloch simulation for optimization of MRI pulse sequences and deep learning-based methods.

Position

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).