Accelerating MRI Using GROG Gridding Followed by ESPIRiT for Non-Cartesian Trajectories
- 作者: Aslam I.1, Najeeb F.1, Omer H.1
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隶属关系:
- Department of Electrical Engineering, COMSATS Institute of Information Technology
- 期: 卷 49, 编号 1 (2018)
- 页面: 107-124
- 栏目: Original Paper
- URL: https://journals.rcsi.science/0937-9347/article/view/247950
- DOI: https://doi.org/10.1007/s00723-017-0943-4
- ID: 247950
如何引用文章
详细
Parallel imaging plays an important role to reduce data acquisition time in magnetic resonance imaging (MRI). Under-sampled non-Cartesian trajectories accelerate the MRI scan time, but the resulting images may have aliasing artifacts. To remove these artifacts, a variety of methods have been developed within the scope of parallel imaging in the recent past. In this paper, the use of Eigen-vector-based iterative Self-consistent Parallel Imaging Reconstruction Technique (ESPIRiT) along with self-calibrated GRAPPA operator gridding (self-calibrated GROG) on radial k-space data for accelerated MR image reconstruction is presented. The proposed method reconstructs the solution image from non-Cartesian k-space data in two steps: First, the acquired radial data is gridded using self-calibrated GROG and then ESPIRIT is applied on this gridded data to get the un-aliased image. The proposed method is tested on human head data and the short-axis cardiac radial data. The quality of the reconstructed images is evaluated using artifact power (AP), root-mean-square error (RMSE) and peak signal-to-noise ratio (PSNR) at different acceleration factors (AF). The results of the proposed method (GROG followed by ESPIRiT) are compared with GROG followed by pseudo-Cartesian GRAPPA reconstruction approach (conventionally used). The results show that the proposed method provides considerable improvement in the reconstructed images as compared to conventionally used pseudo-Cartesian GRAPPA with GROG, e.g., 87, 67 and 82% improvement in terms of AP for 1.5T, 3T human head and short-axis cardiac radial data, 63, 45 and 57% improvement in terms of RMSE for 1.5T, 3T human head and short-axis cardiac radial data, 11, 7 and 9% improvement in terms of PSNR for 1.5T, 3T human head and short-axis cardiac radial data, respectively, at AF = 4.
作者简介
Ibtisam Aslam
Department of Electrical Engineering, COMSATS Institute of Information Technology
编辑信件的主要联系方式.
Email: ibtisam_aslam@yahoo.com
巴基斯坦, Islamabad, 44000
Faisal Najeeb
Department of Electrical Engineering, COMSATS Institute of Information Technology
Email: ibtisam_aslam@yahoo.com
巴基斯坦, Islamabad, 44000
Hammad Omer
Department of Electrical Engineering, COMSATS Institute of Information Technology
Email: ibtisam_aslam@yahoo.com
巴基斯坦, Islamabad, 44000