Ogura Toshiyuki, Hida Kazuhiro, Masuzuka Toshihide, Saito Hisatoshi, Minoshima Satoshi, Nishikawa Kazuhiro
Department of Radiology, Sapporo Azabu Neurosurgical Hospital, East 1, North 40, Higashi-ku, Sapporo, 007-0840, Japan.
Ann Nucl Med. 2009 Jan;23(1):33-41. doi: 10.1007/s12149-008-0203-7. Epub 2009 Feb 11.
We have developed a method to automatically set regions of interest (ROI) (automated ROI) on cerebral blood flow single-photon emission computed tomography (SPECT) images with morphological information specific to the subjects. The objective was to set ROIs automatically without losing individual morphological information in the SPECT images and then evaluate its validity and clinical applicability.
We constructed the volume of interest (VOI) template on the standardized brain generated by NEUROSTAT to determine the regions for ROIs to be set. Assuming patients with cerebral vascular disease, the VOI template was constructed so that the ROIs were drawn for the major vascular regions and 17 regions in total within the hemisphere, basal ganglia, thalamus, cerebellar cortex, cerebellar vermis, and pons. By comparing the major vascular occlusion models, the accuracy of region setting by the VOI template was evaluated for validation. Using the anatomical standardization of NEUROSTAT and inverse transformation, the automated ROI transformed the VOI template into the individual brain shape and then the VOI template was extracted from each slice to determine ROIs. An evaluation was made by visually investigating the effect of a different image quality and cerebral blood flow tracers using brain phantom and clinical data. The regional cerebral blood flow (rCBF), determined by the manual setting method of ROI (manual ROI) and automated ROI, was compared. We also compared automated ROI with other morphological images using clinical data.
The VOI templates accurately showed the region with the reduced blood flow in the major vascular occlusion model, which validated the proper ROI setting. The brain phantom study demonstrated that ROI settings were least influenced by matrix size, image quality, and image rotation. The observation with the clinical data also indicated that the variation in cerebral blood flow tracers little affected the ROI settings. The comparison with manual ROI revealed a strong correlation between the two ROI settings, and the mean values within both ROIs were similar. The comparative evaluation with morphological images, obtained by magnetic resonance imaging (MRI), verified the accurate setting of ROI.
The automated ROI achieved successful automatic ROI settings without distorting individual SPECT images. The automated ROI is not affected by the differences in the image quality or the cerebral blood flow tracers, which suggests versatile applicability. Thus, the use of automated ROI may eliminate the interoperator and interfacility variability in ROI setting and improve objectivity and reproducibility. It also allows comparative evaluation at the same transverse level with images acquired with other modalities such as MRI and is expected to enhance the clinical diagnosis.
我们开发了一种方法，可利用受试者特有的形态学信息在脑血流单光子发射计算机断层扫描（SPECT）图像上自动设置感兴趣区域（ROI）（自动ROI）。目的是在不丢失SPECT图像中个体形态学信息的情况下自动设置ROI，然后评估其有效性和临床适用性。
我们在由NEUROSTAT生成的标准化大脑上构建了感兴趣体积（VOI）模板，以确定要设置ROI的区域。假设为脑血管疾病患者构建VOI模板，以便在半球、基底神经节、丘脑、小脑皮质、小脑蚓部和脑桥内的主要血管区域绘制ROI，总共17个区域。通过比较主要血管闭塞模型，评估VOI模板设置区域的准确性以进行验证。利用NEUROSTAT的解剖标准化和逆变换，自动ROI将VOI模板转换为个体脑形状，然后从每个切片中提取VOI模板以确定ROI。通过使用脑模型和临床数据直观研究不同图像质量和脑血流示踪剂的影响进行评估。比较了通过ROI手动设置方法（手动ROI）和自动ROI确定的局部脑血流（rCBF）。我们还使用临床数据将自动ROI与其他形态学图像进行了比较。
VOI模板准确显示了主要血管闭塞模型中血流减少的区域，这验证了ROI的正确设置。脑模型研究表明，ROI设置受矩阵大小、图像质量和图像旋转的影响最小。临床数据观察也表明，脑血流示踪剂的变化对ROI设置影响很小。与手动ROI的比较显示两种ROI设置之间存在强相关性，并且两个ROI内的平均值相似。与通过磁共振成像（MRI）获得的形态学图像的比较评估验证了ROI的准确设置。
自动ROI在不扭曲个体SPECT图像的情况下成功实现了自动ROI设置。自动ROI不受图像质量或脑血流示踪剂差异的影响，这表明其具有广泛的适用性。因此，使用自动ROI可以消除ROI设置中操作者间和机构间的变异性，提高客观性和可重复性。它还允许与通过MRI等其他模态采集的图像在相同横断面上进行比较评估，并有望增强临床诊断。
