Medical Image Processing Research Group (MIPRG), Department of Electrical & Computer Engineering, COMSATS University Islamabad, Pakistan.
Medical Image Processing Research Group (MIPRG), Department of Electrical & Computer Engineering, COMSATS University Islamabad, Pakistan; Department of Radiology & Medical Informatics, Faculties of Medicine & Life Sciences University of Geneva, Switzerland.
J Magn Reson. 2021 Dec;333:107080. doi: 10.1016/j.jmr.2021.107080. Epub 2021 Oct 12.
This paper presents a new method using tangent vector-based l-regularization for compressed sensing MR image reconstruction.
The proposed method with l-regularization is tested on four datasets: (i) 1-D sparse signal (ii) numerical cardiac phantom, (iii & iv) two sets of in-vivo cardiac MRI datasets acquired using 30 receiver coil elements with Cartesian and radial trajectories on 3T scanner. The results are compared with standard CS reconstruction, which utilizes l-regularization. The experiments were also conducted for two different types of samplings: (i) cartesian sub-sampling and (ii) 2D random Gaussian sub-sampling.
The quality of the reconstructed images is validated through Root Mean Square Error (RMSE) and Peak Signal-to-Noise Ratio (PSNR). The results show that the proposed method outperforms the standard CS reconstructions in our experiments with an improvement of 54.8% in RMSE and 14.3% in terms of PSNR. Moreover, the Gaussian random sub-sampling-based image reconstruction results are better than the Cartesian sub-sampling-based reconstruction results.
The results show that the proposed method yields a good sparse signal approximation and superior convergence behavior, which implies a promising technique for the reconstruction of cardiac MR images as compared to the conventional CS algorithm.
本文提出了一种基于切向量的 l 正则化的新方法，用于压缩感知磁共振图像重建。
该方法采用 l 正则化，在四个数据集上进行了测试：（i）一维稀疏信号；（ii）数值心脏体模；（iii 和 iv）两组在 3T 扫描仪上使用 30 个接收线圈元件采集的体内心脏 MRI 数据集。结果与利用 l 正则化的标准 CS 重建进行了比较。实验还针对两种不同类型的采样进行了：（i）笛卡尔子采样；（ii）二维随机高斯子采样。
通过均方根误差（RMSE）和峰值信噪比（PSNR）验证了重建图像的质量。结果表明，与标准 CS 重建相比，该方法在我们的实验中表现更好，RMSE 提高了 54.8%，PSNR 提高了 14.3%。此外，基于高斯随机子采样的图像重建结果优于基于笛卡尔子采样的重建结果。
结果表明，与传统 CS 算法相比，该方法在稀疏信号逼近和收敛行为方面表现良好，为心脏磁共振图像重建提供了一种很有前途的技术。
