Noise Estimation from Averaged Diffusion Weighted Images: Can Unbiased Quantitative Decay Parameters Assist Cancer Evaluation?
Noise Estimation from Averaged Diffusion Weighted Images: Can Unbiased Quantitative Decay Parameters Assist Cancer Evaluation?
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DOI：
10.1002/mrm.24877
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发表时间：
2014-06-01
影响因子：
3.3
通讯作者：
Atkinson, David
中科院分区：
文献类型：
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作者：
Dikaios, Nikolaos;Punwani, Shonit;Atkinson, David
目的多指数衰减参数是通过扩散加权成像估计的，通常具有固有的低信噪比和非正态噪声分布，尤其是在高b值时。传统的非线性回归算法假设正态分布的噪声，将偏差引入到计算的衰减参数中，并可能影响其对肿瘤进行分类的能力。本研究旨在准确估计平均扩散加权成像的噪声，纠正噪声引起的偏差，并评估对癌症分类的影响。方法提出了对小波域中位绝对偏差技术的新适应，使用卷积概率分布函数的封闭形式近似来估计噪声。考虑潜在噪声（最大概率）的非线性回归算法将双指数/拉伸指数衰减模型拟合到扩散加权信号。一个逻辑回归模型建立从衰减参数来区分良性转移性颈部淋巴结在40 patients.ResultsThe适应中位数绝对偏差方法准确地预测模拟（R-2=0.96）和颈部扩散加权成像（平均一次或四次）的噪声。最大概率恢复真实的表观扩散系数的模拟数据优于非线性回归（高达40%），而没有明显的差异被发现为其他衰减parameters.ConclusionsPerfusion-related参数是最好的癌症分类。噪声校正的衰减参数并没有显着改善临床数据集的分类，虽然模拟显示出较低的信噪比采集的好处。Magn Reson Med 71：2105-2117，2014。(c)2013 Wiley Periodicals，Inc.
PurposeMultiexponential decay parameters are estimated from diffusion-weighted-imaging that generally have inherently low signal-to-noise ratio and non-normal noise distributions, especially at high b-values. Conventional nonlinear regression algorithms assume normally distributed noise, introducing bias into the calculated decay parameters and potentially affecting their ability to classify tumors. This study aims to accurately estimate noise of averaged diffusion-weighted-imaging, to correct the noise induced bias, and to assess the effect upon cancer classification.MethodsA new adaptation of the median-absolute-deviation technique in the wavelet-domain, using a closed form approximation of convolved probability-distribution-functions, is proposed to estimate noise. Nonlinear regression algorithms that account for the underlying noise (maximum probability) fit the biexponential/stretched exponential decay models to the diffusion-weighted signal. A logistic-regression model was built from the decay parameters to discriminate benign from metastatic neck lymph nodes in 40 patients.ResultsThe adapted median-absolute-deviation method accurately predicted the noise of simulated (R-2=0.96) and neck diffusion-weighted-imaging (averaged once or four times). Maximum probability recovers the true apparent-diffusion-coefficient of the simulated data better than nonlinear regression (up to 40%), whereas no apparent differences were found for the other decay parameters.ConclusionsPerfusion-related parameters were best at cancer classification. Noise-corrected decay parameters did not significantly improve classification for the clinical data set though simulations show benefit for lower signal-to-noise ratio acquisitions. Magn Reson Med 71:2105-2117, 2014. (c) 2013 Wiley Periodicals, Inc.