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.

One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction

1 code implementation • 25 Jul 2023 • Zi Wang, Xiaotong Yu, Chengyan Wang, Weibo Chen, Jiazheng Wang, Ying-Hua Chu, Hongwei Sun, Rushuai Li, Peiyong Li, Fan Yang, Haiwei Han, Taishan Kang, Jianzhong Lin, Chen Yang, Shufu Chang, Zhang Shi, Sha Hua, Yan Li, Juan Hu, Liuhong Zhu, Jianjun Zhou, Meijing Lin, Jiefeng Guo, Congbo Cai, Zhong Chen, Di Guo, Guang Yang, Xiaobo Qu

We demonstrate that training DL models on synthetic data, coupled with enhanced learning techniques, yields in vivo MRI reconstructions comparable to or surpassing those of models trained on matched realistic datasets, reducing the reliance on real-world MRI data by up to 96%.

Medical Diagnosis MRI Reconstruction

CloudBrain-MRS: An Intelligent Cloud Computing Platform for in vivo Magnetic Resonance Spectroscopy Preprocessing, Quantification, and Analysis

no code implementations • 19 Jun 2023 • Xiaodie Chen, Jiayu Li, Dicheng Chen, Yirong Zhou, Zhangren Tu, Meijin Lin, Taishan Kang, Jianzhong Lin, Tao Gong, Liuhong Zhu, Jianjun Zhou, Lin Ou-yang, Jiefeng Guo, Jiyang Dong, Di Guo, Xiaobo Qu

We have shared our cloud platform at MRSHub, providing free access and service for two years.

Cloud Computing

Magnetic Resonance Spectroscopy Quantification Aided by Deep Estimations of Imperfection Factors and Macromolecular Signal

no code implementations • 16 Jun 2023 • Dicheng Chen, Meijin Lin, Huiting Liu, Jiayu Li, Yirong Zhou, Taishan Kang, Liangjie Lin, Zhigang Wu, Jiazheng Wang, Jing Li, Jianzhong Lin, Xi Chen, Di Guo, Xiaobo Qu

Methods: Linear Least Squares (LLS) is integrated with deep learning to reduce the complexity of solving this overall quantification.

Cloud Computing

CloudBrain-ReconAI: An Online Platform for MRI Reconstruction and Image Quality Evaluation

no code implementations • 4 Dec 2022 • Yirong Zhou, Chen Qian, Jiayu Li, Zi Wang, Yu Hu, Biao Qu, Liuhong Zhu, Jianjun Zhou, Taishan Kang, Jianzhong Lin, Qing Hong, Jiyang Dong, Di Guo, Xiaobo Qu

Efficient collaboration between engineers and radiologists is important for image reconstruction algorithm development and image quality evaluation in magnetic resonance imaging (MRI).

Cloud Computing MRI Reconstruction

Physics-informed Deep Diffusion MRI Reconstruction with Synthetic Data: Break Training Data Bottleneck in Artificial Intelligence

no code implementations • 20 Oct 2022 • Chen Qian, Yuncheng Gao, Mingyang Han, Zi Wang, Dan Ruan, Yu Shen, Yaping Wu, Yirong Zhou, Chengyan Wang, Boyu Jiang, Ran Tao, Zhigang Wu, Jiazheng Wang, Liuhong Zhu, Yi Guo, Taishan Kang, Jianzhong Lin, Tao Gong, Chen Yang, Guoqiang Fei, Meijin Lin, Di Guo, Jianjun Zhou, Meiyun Wang, Xiaobo Qu

In conclusion, PIDD presents a novel deep learning framework by exploiting the power of MRI physics, providing a cost-effective and explainable way to break the data bottleneck in deep learning medical imaging.

MRI Reconstruction

A Paired Phase and Magnitude Reconstruction for Advanced Diffusion-Weighted Imaging

no code implementations • 28 Mar 2022 • Chen Qian, Zi Wang, Xinlin Zhang, Boxuan Shi, Boyu Jiang, Ran Tao, Jing Li, Yuwei Ge, Taishan Kang, Jianzhong Lin, Di Guo, Xiaobo Qu

Conclusion: The explicit phase model PAIR with complementary priors has a good performance on challenging reconstructions under inter-shot motions between shots and a low signal-to-noise ratio.