Digital Imaging Department, Philips Research, Hamburg, Germany.
Neuroimage. 2010 Apr 15;50(3):994-1003. doi: 10.1016/j.neuroimage.2009.12.056. Epub 2010 Jan 4.
A b-spline-based method 'Lobster', originally designed as a general technique for non-linear image registration, was tailored for stereotactical normalization of brain FDG PET scans. Lobster was compared with the normalization methods of SPM2 and Neurostat with respect to the impact on the accuracy of voxel-based statistical analysis. (i) Computer simulation: Seven representative patterns of cortical hypometabolism served as artificial ground truth. They were inserted into 26 normal control scans with different simulated severity levels. After stereotactical normalization and voxel-based testing, statistical maps were compared voxel-by-voxel with the ground truth. This was done at different levels of statistical significance. There was a highly significant effect of the stereotactical normalization method on the area under the resulting ROC curve. Lobster showed the best average performance and was most stable with respect to variation of the severity level. (ii) Clinical evaluation: Statistical maps were obtained for the normal controls as well as patients with Alzheimer's disease (AD, n=44), Lewy-Body disease (LBD, 9), fronto-temporal dementia (FTD, 13), and cortico-basal dementia (CBD, 4). These maps were classified as normal, AD, LBD, FTD, or CBD by two experienced readers. The stereotactical normalization method had no significant effect on classification by of each of the experts, but it appeared to affect agreement between the experts. In conclusion, Lobster is appropriate for use in single-subject analysis of brain FDG PET scans in suspected dementia, both in early diagnosis (mild hypometabolism) and in differential diagnosis in advanced disease stages (moderate to severe hypometabolism). The computer simulation framework developed in the present study appears appropriate for quantitative evaluation of the impact of the different processing steps and their interaction on the performance of voxel-based single-subject analysis.
一种基于样条的方法“龙虾”，最初设计为一种通用的非线性图像配准技术，被专门用于脑 FDG PET 扫描的立体定向归一化。就基于体素的统计分析的准确性的影响而言，龙虾与 SPM2 和 Neurostat 的归一化方法进行了比较。(i) 计算机模拟：七种具有代表性的皮质代谢低下模式被用作人工的真实情况。它们被插入到 26 例不同模拟严重程度的正常对照扫描中。经过立体定向归一化和基于体素的测试后，通过体素与真实情况比较统计地图。在不同的统计显著性水平上进行了这一操作。立体定向归一化方法对产生的 ROC 曲线下面积的影响有显著的影响。龙虾表现出最好的平均性能，并且对严重程度的变化最稳定。(ii) 临床评估：为正常对照者以及阿尔茨海默病患者（AD，n=44）、路易体病患者（LBD，9）、额颞叶痴呆患者（FTD，13）和皮质基底节变性患者（CBD，4）获得了统计地图。由两位经验丰富的读者将这些地图分为正常、AD、LBD、FTD 或 CBD。立体定向归一化方法对每位专家的分类都没有显著影响，但它似乎影响了专家之间的一致性。总之，龙虾适用于疑似痴呆症的脑 FDG PET 扫描的单例分析，无论是早期诊断（轻度代谢低下）还是晚期疾病阶段的鉴别诊断（中度至重度代谢低下）。本研究中开发的计算机模拟框架似乎适合于定量评估不同处理步骤及其相互作用对基于体素的单例分析性能的影响。
