Zhu Zangen, Wahid Khan, Babyn Paul, Yang Ran
Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK, Canada S7N 5A9.
Int J Biomed Imaging. 2013;2013:907501. doi: 10.1155/2013/907501. Epub 2013 Jun 6.
Undersampling k-space data is an efficient way to speed up the magnetic resonance imaging (MRI) process. As a newly developed mathematical framework of signal sampling and recovery, compressed sensing (CS) allows signal acquisition using fewer samples than what is specified by Nyquist-Shannon sampling theorem whenever the signal is sparse. As a result, CS has great potential in reducing data acquisition time in MRI. In traditional compressed sensing MRI methods, an image is reconstructed by enforcing its sparse representation with respect to a basis, usually wavelet transform or total variation. In this paper, we propose an improved compressed sensing-based reconstruction method using the complex double-density dual-tree discrete wavelet transform. Our experiments demonstrate that this method can reduce aliasing artifacts and achieve higher peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) index.
欠采样k空间数据是加速磁共振成像（MRI）过程的一种有效方法。作为一种新开发的信号采样和恢复数学框架，压缩感知（CS）允许在信号稀疏时使用比奈奎斯特 - 香农采样定理规定的更少样本进行信号采集。因此，CS在减少MRI数据采集时间方面具有巨大潜力。在传统的压缩感知MRI方法中，通过强制图像相对于某个基（通常是小波变换或总变差）的稀疏表示来重建图像。在本文中，我们提出了一种使用复数双密度双树离散小波变换的改进的基于压缩感知的重建方法。我们的实验表明，该方法可以减少混叠伪影，并实现更高的峰值信噪比（PSNR）和结构相似性（SSIM）指数。
