A New Informatics Approach for Detection of Cerebrovascular Abnormalities

这个临床信息学项目的目标是发展计算技术来模拟和分析大脑 用于检测作为脑血管疾病（CVD）标志的形态测量异常的血管。 该项目解决了神经放射学和神经外科的一个重要挑战：如何准确诊断 计算机断层扫描血管造影（CTA）上的CVD。心血管疾病包括颅内动脉瘤、中风、颅内动脉瘤、颅内动脉瘤、脑血管病和其他疾病。 血管狭窄、硬脑膜瘘和脑血管系统的其他病症，并且这些疾病具有严重的 结果，因为它们导致出血、中风、神经损伤和死亡。事实上，每年，心血管疾病导致 美国有超过10万人死亡，还有更多的人遭受永久性伤害，包括 中风瘫痪和失语如果我们能更准确、更迅速地诊断心血管疾病， 发病率可以显著降低。 脑成像是心血管病的一线诊断，其图像标志是脑血管 异常然而，诊断是非常具有挑战性的，因为临床医生需要筛选和放大和缩小 旋转大量的图像来检查每一个血管的畸形，无论它是一个 狭窄或在血管壁上形成颅内动脉瘤。同样，神经外科医生需要 在手术前阅读脑部扫描图，以确定异常的位置。 我们这个项目的具体目标是开发新的计算技术，包括深度学习， 对血管进行建模和分析，以检测异常并突出显示其位置，供临床医生检查 进一步.虽然计算机还不足以像训练有素的临床医生那样进行诊断， 与人类专家相比，计算机可以更客观、更快速地执行任务， 形状分析和量化，例如识别血管的异常变宽或变窄， 检测血管壁上的突起。为了满足临床医生的要求， 显著地来自异常的计算机辅助检测，并且一旦异常被标记，它们可以 为了对潜在的心血管疾病进行高度准确的诊断和分类，我们设计了一个信息学 方法作为计算机辅助工具来分析CTA图像。我们将模拟单个血管， 在3D空间中的整个脉管系统。然后，从脉管系统，我们将开发和实施一个多- 通道深度学习模型专注于形状分析，以检测血管异常。最后， 异常将在3D中以颜色标记，以允许临床医生做出更准确的诊断，计划 预防性治疗，并进行精确的手术，以造福患者的健康。

The goal of this clinical informatics project is to develop computational techniques to model and analyze brain blood vessels for detecting morphometric abnormalities that are hallmarks of cerebrovascular diseases (CVDs). The project addresses an important challenge in neuroradiology and neurosurgery: how to accurately diagnose CVDs on computed tomography angiography (CTA). CVDs include intracranial aneurysms, stroke, intracranial vascular stenosis, dural fistula, and other disorders of the brain vasculature, and these diseases have severe outcomes as they cause hemorrhage, stroke, neurological damage, and death. In fact, each year, CVDs cause more than 100,000 deaths in the US, and an even larger population suffers permanent damage, including stroke, paralysis, and loss of speech. If we can diagnose CVDs more accurately and promptly, mortality and morbidity can be significantly reduced. Brain imaging is a first line diagnostic for CVDs with the image hallmarks being brain blood vessel abnormalities. Yet diagnosis is very challenging because a clinician needs to sift through and zoom in and out of and rotate a large number of images to examine each blood vessel for malformation, whether it is a narrowing or the formation of intracranial aneurysms on blood vessel walls. Similarly, a neurosurgeon needs to read brain scans right before an operation to locate the positions of abnormalities. Our specific aims of this project are to develop novel computational techniques including deep learning to model and analyze blood vessels to detect abnormalities and highlight their locations for clinicians to examine further. While computers are not yet sophisticated enough to make diagnoses like a trained clinician, computers can perform more objectively and quickly, compared to human experts, the necessary complex shape analysis and quantification, such as identifying abnormal widening or narrowing of blood vessels and detecting protrusions on blood vessel walls. To address the request from clinicians that they would benefit significantly from computer-aided detection of abnormalities and, once abnormalities are marked, they can make highly accurate diagnosis and classification of the underlying CVDs, we designed an informatics approach as a computer-aided tool to analyze CTA images. We will model both individual blood vessels and the whole vasculature in the 3D space. Then, from the vasculature, we will develop and implement a multi- channel deep learning model focused on shape analysis to detect blood vessel abnormalities. Finally, abnormalities will be marked in colors in 3D to allow clinicians to make more accurate diagnoses, plan preventative treatments, and perform precise surgeries to benefit patient health.