Liu Zhexing, Goodlett Casey, Gerig Guido, Styner Martin
Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA 27510.
Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA 84408.
Proc SPIE Int Soc Opt Eng. 2010 Mar 12;7623:762325-. doi: 10.1117/12.844911.
Compared to region of interest based DTI analysis, voxel-based analysis gives higher degree of localization and avoids the procedure of manual delineation with the resulting intra and inter-rater variability. One of the major challenges in voxel-wise DTI analysis is to get high quality voxel-level correspondence. For that purpose, current DTI analysis tools are building on nonlinear registration algorithms that deform individual datasets into a template image that is either precomputed or computed as part of the analysis. A variety of matching criteria and deformation schemes have been proposed, but often comparative evaluation is missing. In our opinion, the use of consistent and unbiased measures to evaluate current DTI procedures is of great importance and our work presents two possible measures. Specifically, we propose the evaluation criteria generalization and specificity, originally introduced by the shape modeling community, to evaluate and compare different DTI nonlinear warping results. These measures are of indirect nature and have a population wise view. Both measures incorporate information of the variability of the registration results in the template space via a voxel-wise PCA model. Thus far, we have used these measures to evaluate our own DTI analysis procedure employing fluid-based registration on scalar DTI maps. Generalization and specificity from tensor images in the template space were computed for 8 scalar property maps. We found that for our procedure an intensity-normalized FA feature outperformed the other scalar measurements. Also, using the tensor images rather than the FA maps as a comparison frame seemed to produce more robust results.
与基于感兴趣区域的扩散张量成像（DTI）分析相比，基于体素的分析具有更高的定位精度，并且避免了手动勾勒过程中因不同评分者之间以及同一评分者内部差异而导致的问题。体素级DTI分析的主要挑战之一是获得高质量的体素级对应关系。为此，当前的DTI分析工具基于非线性配准算法构建，这些算法将单个数据集变形为预先计算或作为分析一部分计算的模板图像。已经提出了各种匹配标准和变形方案，但往往缺少比较评估。我们认为，使用一致且无偏差的度量来评估当前的DTI程序非常重要，我们的工作提出了两种可能的度量。具体而言，我们提出了最初由形状建模社区引入的评估标准——泛化性和特异性，以评估和比较不同的DTI非线性扭曲结果。这些度量具有间接性质，并且从总体角度出发。这两种度量都通过体素级主成分分析（PCA）模型纳入了模板空间中配准结果变异性的信息。到目前为止，我们已经使用这些度量来评估我们自己在标量DTI图上采用基于流体配准的DTI分析程序。针对8个标量属性图计算了模板空间中张量图像的泛化性和特异性。我们发现，对于我们的程序，强度归一化的各向异性分数（FA）特征优于其他标量测量。此外，使用张量图像而非FA图作为比较框架似乎能产生更稳健的结果。
