Online reconstruction of fast dynamic MR imaging using deep low-rank plus sparse network
- Resource Type
- Conference
- Authors
- Wang, Che; Jia, Sen; Yan, Zhonghong; Zheng, Yijia; Liu, Shaonan; Wang, Haifeng; Liang, Dong; Zhu, Yanjie
- Source
- 2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS) CBMS Computer-Based Medical Systems (CBMS), 2022 IEEE 35th International Symposium on. :166-170 Jul, 2022
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Sensitivity
Software packages
Graphics processing units
Estimation
Signal processing
Reconstruction algorithms
Image reconstruction
L+S-Net
online dynamic MR imaging
Gadgetron
SigPy GPU
- Language
- ISSN
- 2372-9198
In order to test the performance of online reconstruction of deep low-rank pulse sparse network (L+S-Net) for fast dynamic MR imaging. The L+S-Net was implemented on Gadgetron platform for online reconstruction of the scanner. Although L+S-net has a good image reconstruction performance., it takes a long time to estimate the coil sensitivity using ESPIRiT method. In this study, SigPy's signal processing software package was adopted to accelerate the calculation of coil sensitivity to speed up the online reconstruction. The results of experiments showed that compared with the CPU based method., the time of the coil sensitivity estimation could be shortened more than 100 times by using the gridding reconstruction method based on SigPy GPU. The reconstruction performance is stable and can realize online fast dynamic MR imaging reconstruction within 10 seconds.