Institute of Medical Engineering, Graz University of Technology, Stremayrgasse 16, 8010 Graz, Austria.
Institute for Mathematics and Scientific Computing, University of Graz, NAWI Graz, Heinrichstrasse 36, 8010 Graz, Austria.
Neuroimage. 2017 Aug 15;157:81-96. doi: 10.1016/j.neuroimage.2017.05.054. Epub 2017 May 27.
In arterial spin labeling (ASL) a perfusion weighted image is achieved by subtracting a label image from a control image. This perfusion weighted image has an intrinsically low signal to noise ratio and numerous measurements are required to achieve reliable image quality, especially at higher spatial resolutions. To overcome this limitation various denoising approaches have been published using the perfusion weighted image as input for denoising. In this study we propose a new spatio-temporal filtering approach based on total generalized variation (TGV) regularization which exploits the inherent information of control and label pairs simultaneously. In this way, the temporal and spatial similarities of all images are used to jointly denoise the control and label images. To assess the effect of denoising, virtual ground truth data were produced at different SNR levels. Furthermore, high-resolution in-vivo pulsed ASL data sets were acquired and processed. The results show improved image quality, quantitative accuracy and robustness against outliers compared to seven state of the art denoising approaches.
在动脉自旋标记（ASL）中，通过从对照图像中减去标记图像来获得灌注加权图像。该灌注加权图像固有地具有低信噪比，并且需要进行多次测量才能获得可靠的图像质量，尤其是在更高的空间分辨率下。为了克服这一限制，已经使用各种去噪方法来发布，将灌注加权图像作为输入用于去噪。在这项研究中，我们提出了一种新的基于全广义变分（TGV）正则化的时空滤波方法，该方法同时利用了控制和标记对的固有信息。通过这种方式，利用所有图像的时间和空间相似性来共同对控制和标记图像进行去噪。为了评估去噪效果，在不同 SNR 水平下生成了虚拟真实数据。此外，还采集和处理了高分辨率的体内脉冲 ASL 数据集。结果表明，与七种最先进的去噪方法相比，该方法在图像质量、定量准确性和抗异常值能力方面均有所提高。
