Palumbo Daniel, Yee Brian, O'Dea Patrick, Leedy Shane, Viswanath Satish, Madabhushi Anant
Department of Biomedical Engineering, Rutgers University, The State University of New Jersey, 599 Taylor Road, Piscataway, NJ 08854, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:5080-3. doi: 10.1109/IEMBS.2011.6091258.
Magnetic Resonance Imaging (MRI) is known to be significantly affected by a number of acquisition artifacts, such as intensity non-standardness, bias field, and Gaussian noise. These artifacts degrade MR image quality significantly, obfuscating anatomical and physiological detail and hence need to be corrected for to facilitate application of computerized analysis techniques such as segmentation, registration, and classification. Specifically, algorithms are required to correct for bias field (intensity inhomogeneity), intensity non-standardness (drift in tissue intensities across patient acquisitions), and Gaussian noise, an artifact that significantly affects and blurs tissue boundaries (resulting in poor gradients). While clearly one needs to correct for all these artifacts, the exact sequence in which all three operations need to be applied in order to maximize MR image quality has not been explored. In this paper, we empirically evaluate the interplay between distinct algorithms for bias field correction (BFC), intensity standardization (IS), and noise filtering (NF) to study the effect of these operations on image quality in the context of 3 Tesla T2-weighted (T2w) prostate MRI. 7 different sequences comprising combinations of BFC, IS, and NF were quantitatively evaluated in terms of the percent coefficient of variation (%CV), a statistic which attempts to quantify the intensity inhomogeneity within a region of interest (prostate). The different combinations were also independently evaluated in the context of a classifier scheme for detection of prostate cancer on high resolution in vivo T2w prostate MRI. A secondary contribution of this work is a novel evaluation measure for quantifying the level of intensity non-standardness, called difference of modes (DoM). Experimental evaluation of the different sequences of operations across 22 patient datasets revealed that the sequence of BFC, followed by NF, and IS provided the best image quality in terms of %CV as well as classifier accuracy. The DoM measure was able to accurately capture the level of intensity non-standardness present in the images resulting from the different sequences of operations.
众所周知，磁共振成像（MRI）会受到许多采集伪影的显著影响，例如强度非标准化、偏置场和高斯噪声。这些伪影会严重降低MR图像质量，模糊解剖和生理细节，因此需要进行校正，以促进诸如分割、配准和分类等计算机分析技术的应用。具体而言，需要算法来校正偏置场（强度不均匀性）、强度非标准化（患者采集过程中组织强度的漂移）和高斯噪声，高斯噪声是一种会显著影响并模糊组织边界（导致梯度不佳）的伪影。虽然显然需要校正所有这些伪影，但尚未探索为了最大化MR图像质量而应用这三种操作的准确顺序。在本文中，我们通过实证评估用于偏置场校正（BFC）、强度标准化（IS）和噪声滤波（NF）的不同算法之间的相互作用，以研究这些操作在3特斯拉T2加权（T2w）前列腺MRI背景下对图像质量的影响。根据变异系数百分比（%CV）对包含BFC、IS和NF组合的7种不同序列进行了定量评估，%CV是一种试图量化感兴趣区域（前列腺）内强度不均匀性的统计量。还在用于高分辨率体内T2w前列腺MRI上检测前列腺癌的分类器方案背景下对不同组合进行了独立评估。这项工作的第二个贡献是一种用于量化强度非标准化水平的新颖评估方法，称为模式差异（DoM）。对22个患者数据集上不同操作序列的实验评估表明，先进行BFC，然后进行NF，最后进行IS的序列在%CV以及分类器准确性方面提供了最佳图像质量。DoM测量能够准确捕捉由不同操作序列产生的图像中存在的强度非标准化水平。
