摘要
动脉自旋标记(ASL)MR图像信噪比低,需要重复采集多次以获得高质量的血流(CBF)图。临床中通常使用3D高斯滤波降低噪声但效果不佳。鉴于此,提出了新的基于非局域均值滤波(NLM)的ASL图像去噪方法,利用图像内部块相似度加权,降低噪声并提高血流图的计算精度。实验证明:与高斯滤波的结果比较,采用新方法得到的血流图和真实结果更接近,实现了在较少采集次数的情况下,得到精确的血流图像的目标。
Arterial Spin Labeling（ASL）MR images suffer from low SNR. In order to achieve adequate high image quality, it is necessary to execute experiment a number of times repeatedly. 3D Gaussian filter is widely in clinical use but the fil-ter effect is not good enough. A new ASL image denoising algorithm based on Non-Local Means（NLM）denoising meth-od is proposed. The new denoising method uses region similarity weight to decrease SNR and increases the accuracy of Cerebral Blood Flow（CBF）map. The results of CBF maps indicate that the CBF map obtained by the proposed method is more close to the real result than the Gaussian smoothing, which realizes the goal of obtaining accurate CBF map in the case of less repeated times.
出处
《计算机工程与应用》
CSCD
2014年第1期159-162,共4页
Computer Engineering and Applications
基金
国家自然科学基金(No.81000635)
