Denoising of Dynamic Contrast-Enhanced MR Images Using Dynamic Nonlocal Means
Denoising of Dynamic Contrast-Enhanced MR Images Using Dynamic Nonlocal Means
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DOI：
10.1109/tmi.2009.2026575
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发表时间：
2010-02-01
影响因子：
10.6
通讯作者：
Crozier, Stuart
中科院分区：
文献类型：
--
作者：
Gal, Yaniv;Mehnert, Andrew J. H.;Crozier, Stuart
提出了一种新的动态对比度增强（DCE） MR图像去噪算法。它是对非局部均值（NLM）算法的一种新的改进。该算法被称为动态非局部均值（DNLM），利用图像时间序列中的信息冗余。本文给出了DNLM算法相对于其他七种降噪方法（简单高斯滤波、原始NLM算法、NLM的简单扩展以包括时间维度、双边滤波、各向异性扩散滤波、小波自适应多尺度积阈值和传统小波阈值）的性能的经验评估。评估包括使用模拟数据和真实数据（来自常规临床乳腺MRI检查的20组DCE-MRI数据集）进行定量评估，以及使用相同真实数据（24名观察员：14名图像/信号处理专家，10名临床乳腺MRI放射技师）进行定性评估。利用模拟数据进行定量评价的结果表明，DNLM算法在去噪图像和相应的原始无噪图像之间始终产生最小的MSE。使用真实数据进行定量评价的结果表明，在alpha = 0.05的显著性水平下，DNLM算法产生的去噪图像与其相应的原始无噪图像之间的MSE最小。定性评价的结果提供了证据，在alpha = 0.05的显著性水平上，DNLM算法在视觉上优于所有其他算法。定性和定量结果表明，DNLM算法比任何其他算法更有效地衰减DCE MR图像中的噪声。
This paper presents a new algorithm for denoising dynamic contrast-enhanced (DCE) MR images. It is a novel variation on the nonlocal means (NLM) algorithm. The algorithm, called dynamic nonlocal means (DNLM), exploits the redundancy of information in the temporal sequence of images. Empirical evaluations of the performance of the DNLM algorithm relative to seven other denoising methods-simple Gaussian filtering, the original NLM algorithm, a trivial extension of NLM to include the temporal dimension, bilateral filtering, anisotropic diffusion filtering, wavelet adaptive multiscale products threshold, and traditional wavelet thresholding-are presented. The evaluations include quantitative evaluations using simulated data and real data (20 DCE-MRI data sets from routine clinical breast MRI examinations) as well as qualitative evaluations using the same real data (24 observers: 14 image/signal-processing specialists, 10 clinical breast MRI radiographers). The results of the quantitative evaluation using the simulated data show that the DNLM algorithm consistently yields the smallest MSE between the denoised image and its corresponding original noiseless version. The results of the quantitative evaluation using the real data provide evidence, at the alpha = 0.05 level of significance, that the DNLM algorithm yields the smallest MSE between the denoised image and its corresponding original noiseless version. The results of the qualitative evaluation provide evidence, at the alpha = 0.05 level of significance, that the DNLM algorithm performs visually better than all of the other algorithms. Collectively the qualitative and quantitative results suggest that the DNLM algorithm more effectively attenuates noise in DCE MR images than any of the other algorithms.