Denoising of arterial spin labeling data: wavelet-domain filtering compared with Gaussian smoothing
Denoising of arterial spin labeling data: wavelet-domain filtering compared with Gaussian smoothing
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DOI：
10.1007/s10334-010-0209-8
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发表时间：
2010-06-01
影响因子：
2.3
通讯作者：
Wirestam, Ronnie
中科院分区：
文献类型：
--
作者：
Bibic, Adnan;Knutsson, Linda;Wirestam, Ronnie
研究一种基于小波的动脉自旋标记(ASL)数据去噪方法，以减少所需的平均次数和采集时间。ASL磁共振成像(MRI)通过使用动脉水作为内源性示踪剂来提供定量的血流灌注图。标记图像和对照图像之间的信号差异，其中流入的动脉自旋是反向的，而对照图像与血液灌注量成比例。ASL灌注图存在信噪比低的问题，必须重复多次(通常超过40次)才能获得足够的图像质量。研究了小波域滤波方法在模拟和实验图像数据中引入的系统误差，并与传统的高斯平滑方法进行了比较。在保留标准差的情况下，小波域滤波方法的平均次数和采集时间至少减少了50%，但对边界和边缘附近的CBF值有影响。当ASL灌注图显示中到高信噪比时，在灰质和白质边界附近，小波域滤波优于高斯平滑，而对于较大的均匀区域，无论信噪比如何，高斯平滑是更好的选择。
To investigate a wavelet-based filtering scheme for denoising of arterial spin labeling (ASL) data, potentially enabling reduction of the required number of averages and the acquisition time.ASL magnetic resonance imaging (MRI) provides quantitative perfusion maps by using arterial water as an endogenous tracer. The signal difference between a labeled image, where inflowing arterial spins are inverted, and a control image is proportional to blood perfusion. ASL perfusion maps suffer from low SNR, and the experiment must be repeated a number of times (typically more than 40) to achieve adequate image quality. In this study, systematic errors introduced by the proposed wavelet-domain filtering approach were investigated in simulated and experimental image datasets and compared with conventional Gaussian smoothing.Application of the proposed method enabled a reduction of the number of averages and the acquisition time by at least 50% with retained standard deviation, but with effects on absolute CBF values close to borders and edges.When the ASL perfusion maps showed moderate-to-high SNRs, wavelet-domain filtering was superior to Gaussian smoothing in the vicinity of borders between gray and white matter, while Gaussian smoothing was a better choice for larger homogeneous areas, irrespective of SNR.