A New Informatics Approach for Detection of Cerebrovascular Abnormalities

这个临床信息学项目的目标是开发计算技术来模拟和分析大脑 用于检测形态异常的血管，这些异常是脑血管疾病(CVD)的标志。 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 CT血管造影术(CTA)显示脑血管畸形。心血管疾病包括颅内动脉瘤、中风、颅内 血管狭窄、硬脑膜瘘等脑血管疾病，这些疾病都有严重的 导致出血、中风、神经损伤和死亡的后果。事实上，每年，心血管疾病都会导致 美国有超过10万人死亡，更多的人遭受永久性损害，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，死亡率和 发病率可以显著降低。 脑成像是脑血管疾病的一线诊断方法，其影像特征是脑血管 异常现象。然而，诊断是非常具有挑战性的，因为临床医生需要筛选并放大和缩小 旋转大量的图像以检查每条血管的畸形，无论它是 血管壁上的颅内动脉瘤变窄或形成。同样，神经外科医生需要 Read大脑扫描就在手术前定位异常的位置。 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，以供临床医生检查 再远一点。虽然计算机还不够复杂，不能像训练有素的临床医生那样做出诊断， 与人类专家相比，计算机可以更客观、更快速地执行必要的复杂操作 形状分析和量化，如识别异常的血管增宽或狭窄和 检测血管壁上的突起。解决临床医生提出的他们将受益的请求 显著地来自计算机辅助的异常检测，一旦异常被标记，它们就可以 对潜在的心血管疾病进行高精度的诊断和分类，我们设计了一种信息学 方法作为分析CTA图像的计算机辅助工具。我们将对单个血管和 3D空间中的整个血管系统。然后，从血管系统出发，我们将开发和实施一种多- 通道深度学习模型侧重于形状分析，以检测血管异常。最后， 异常将以3D颜色标记，使临床医生能够做出更准确的诊断、计划 预防性治疗，并进行精确的手术，以利于患者的健康。

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.