Ganzetti Marco, Wenderoth Nicole, Mantini Dante
Neural Control of Movement Laboratory, ETH ZurichZurich, Switzerland; Department of Experimental Psychology, University of OxfordOxford, UK.
Neural Control of Movement Laboratory, ETH Zurich Zurich, Switzerland.
Front Neuroinform. 2016 Mar 15;10:10. doi: 10.3389/fninf.2016.00010. eCollection 2016.
Intensity non-uniformity (INU) in magnetic resonance (MR) imaging is a major issue when conducting analyses of brain structural properties. An inaccurate INU correction may result in qualitative and quantitative misinterpretations. Several INU correction methods exist, whose performance largely depend on the specific parameter settings that need to be chosen by the user. Here we addressed the question of how to select the best input parameters for a specific INU correction algorithm. Our investigation was based on the INU correction algorithm implemented in SPM, but this can be in principle extended to any other algorithm requiring the selection of input parameters. We conducted a comprehensive comparison of indirect metrics for the assessment of INU correction performance, namely the coefficient of variation of white matter (CVWM), the coefficient of variation of gray matter (CVGM), and the coefficient of joint variation between white matter and gray matter (CJV). Using simulated MR data, we observed the CJV to be more accurate than CVWM and CVGM, provided that the noise level in the INU-corrected image was controlled by means of spatial smoothing. Based on the CJV, we developed a data-driven approach for selecting INU correction parameters, which could effectively work on actual MR images. To this end, we implemented an enhanced procedure for the definition of white and gray matter masks, based on which the CJV was calculated. Our approach was validated using actual T1-weighted images collected with 1.5 T, 3 T, and 7 T MR scanners. We found that our procedure can reliably assist the selection of valid INU correction algorithm parameters, thereby contributing to an enhanced inhomogeneity correction in MR images.
在进行脑结构特性分析时，磁共振（MR）成像中的强度不均匀性（INU）是一个主要问题。不准确的INU校正可能导致定性和定量的错误解读。存在几种INU校正方法，其性能在很大程度上取决于用户需要选择的特定参数设置。在这里，我们解决了如何为特定的INU校正算法选择最佳输入参数的问题。我们的研究基于SPM中实现的INU校正算法，但原则上这可以扩展到任何其他需要选择输入参数的算法。我们对评估INU校正性能的间接指标进行了全面比较，即白质变异系数（CVWM）、灰质变异系数（CVGM）以及白质和灰质之间的联合变异系数（CJV）。使用模拟MR数据，我们观察到，如果通过空间平滑控制INU校正图像中的噪声水平，CJV比CVWM和CVGM更准确。基于CJV，我们开发了一种数据驱动的方法来选择INU校正参数，该方法可以有效地应用于实际MR图像。为此，我们实施了一种增强的程序来定义白质和灰质掩码，并在此基础上计算CJV。我们的方法使用1.5T、3T和7T MR扫描仪采集的实际T1加权图像进行了验证。我们发现我们的程序可以可靠地辅助选择有效的INU校正算法参数，从而有助于增强MR图像中的不均匀性校正。
