Matsuda Hiroshi
Department of Nuclear Medicine, Saitama Medical University Hospital, 38 Morohongo, Moroyama cho, Iruma gun, Saitama 350 0495, Japan.
Brain Nerve. 2007 May;59(5):487-93.
We developed a method for automated diagnosis of brain perfusion SPECT and designated this method as an easy Z-score imaging system (eZIS). In this software program, voxel-by-voxel Z-score analysis after voxel normalization to global mean or cerebellar values; Z-score = ( [control mean] - [individual value] )/ (control SD) is performed. These Z-score maps are displayed by overlay on tomographic sections and by projection with averaged Z-score of 14mm thickness to surface rendering of the anatomically standardized MRI template. Anatomical standardization of SPECT images into a stereotactic space is performed using statistical parametric mapping (SPM) 2. This program has an advantage of capability of incorporation of SPM results into automated analysis of Z-score values as a volume of interest (VOI). A specific VOI can be determined by group comparison of SPECT images for patients with a neuropsychiatric disease with those for healthy volunteers using SPM. Even if a center can construct a normal database with good quality comprising a large number of healthy volunteers, other centers have not been able to use this normal database because of differences between the used gamma cameras, collimators and physical correction algorithms. Since SPECT exhibits greater variations in image quality among different centers than PET, conversion of SPECT images may be necessary for sharing a normal database. In this eZIS software, we incorporated a newly developed program for making it possible to share a normal database in SPECT studies. A Hoffman 3-dimensional brain phantom experiment was conducted to determine systematic differences between SPECT scanners. SPECT images for the brain phantom were obtained using two different scanners. Dividing these two phantom images after anatomical standardization by SPM created a 3-dimensional conversion map. The use of a conversion map obtained from SPECT images of the same phantom provided very similar SPECT data despite extreme differences between scanners. The present method may be useful for combining normal databases from different centers and greatly enhance the diagnostic value of brain SPECT imaging by standardization of data analysis using a common normal database.
我们开发了一种用于脑灌注单光子发射计算机断层扫描（SPECT）自动诊断的方法，并将此方法命名为简易Z评分成像系统（eZIS）。在这个软件程序中，先将体素归一化为全局均值或小脑值，然后逐体素进行Z评分分析；Z评分 =（[对照均值] - [个体值]）/（对照标准差）。这些Z评分图通过叠加在断层切片上以及以14毫米厚度的平均Z评分投影到解剖学标准化的磁共振成像（MRI）模板的表面渲染图上进行显示。使用统计参数映射（SPM）2将SPECT图像进行解剖学标准化到立体定向空间。该程序的一个优点是能够将SPM结果作为感兴趣区（VOI）纳入Z评分值的自动分析中。可以通过使用SPM对患有神经精神疾病的患者与健康志愿者的SPECT图像进行组间比较来确定特定的VOI。即使一个中心能够构建一个包含大量健康志愿者的高质量正常数据库，但由于所使用的伽马相机、准直器和物理校正算法存在差异，其他中心也无法使用这个正常数据库。由于与正电子发射断层扫描（PET）相比，SPECT在不同中心之间的图像质量差异更大，因此可能需要对SPECT图像进行转换以共享正常数据库。在这个eZIS软件中，我们纳入了一个新开发的程序，使得在SPECT研究中能够共享正常数据库。进行了一项霍夫曼三维脑体模实验，以确定SPECT扫描仪之间的系统差异。使用两台不同的扫描仪获取脑体模的SPECT图像。通过SPM对这两个体模图像进行解剖学标准化后进行分割，创建了一个三维转换图。尽管扫描仪之间存在极大差异，但使用从相同体模的SPECT图像获得的转换图可提供非常相似的SPECT数据。本方法可能有助于合并来自不同中心的正常数据库，并通过使用共同的正常数据库进行数据分析标准化，大大提高脑SPECT成像的诊断价值。
