Traumatic brain injury (TBI) is a major cause of mortality and morbidity, placing a significant financial burden on the healthcare system worldwide. Non-invasive neuroimaging technologies have been playing a pivotal role in the study of TBI, providing important information for surgical planning and patient management. Advances in understanding the basic mechanisms and pathophysiology of the brain following TBI are hindered by a lack of reliable image analysis methods for accurate quantitative assessment of TBI-induced structural and pathophysiological changes seen on anatomical and functional images obtained from multiple imaging modalities. Conventional region-of-interest (ROI) analysis based on manual labeling of brain regions is time-consuming and the results could be inconsistent within and among investigators. In this study, we propose a workflow solution framework that combined the use of non-linear spatial normalization of structural brain images and template-based anatomical labeling to automate the ROI analysis process. The proposed workflow solution is applied to dynamic PET scanning with 15O-water (0-10 min) and 18F-FDDNP (0-6 min) for measuring cerebral blood flow in patients with TBI.

中文翻译：

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多模式神经影像的半自动化工作流程解决方案：应用于创伤性脑损伤患者。


创伤性脑损伤 (TBI) 是导致死亡和发病的主要原因，给全球医疗保健系统带来了巨大的经济负担。无创神经影像技术在 TBI 研究中发挥着关键作用，为手术计划和患者管理提供重要信息。由于缺乏可靠的图像分析方法来准确定量评估从多种成像方式获得的解剖和功能图像上看到的 TBI 引起的结构和病理生理变化，阻碍了对 TBI 后大脑基本机制和病理生理学的理解的进展。基于手动标记大脑区域的传统感兴趣区域 (ROI) 分析非常耗时，而且研究人员内部和研究人员之间的结果可能不一致。在本研究中，我们提出了一个工作流程解决方案框架，该框架结合使用结构脑图像的非线性空间归一化和基于模板的解剖标记来自动化 ROI 分析过程。所提出的工作流程解决方案适用于使用 15O-水（0-10 分钟）和 18F-FDDNP（0-6 分钟）进行动态 PET 扫描，以测量 TBI 患者的脑血流量。