A New Informatics Approach for Detection of Cerebrovascular Abnormalities

检测脑血管异常的新信息学方法

• 批准号: 10682493
• 负责人: Geoffrey Young
• 金额: $ 38.04万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10682493
• 关键词: 3-Dimensional; Address; Adoption; Affect; Algorithms; Angiography; Arteries; Back; Blood Vessels; Brain; Brain Aneurysms; Brain Diseases; Brain hemorrhage; Brain imaging; Brain scan; Cause of Death; Centers for Disease Control and Prevention (U.S.); Cerebral Angiography; Cerebrovascular Disorders; Cerebrovascular system; Cessation of life; Classification; Clinic; Clinical; Clinical Informatics; Coagulation Process; Color; Complex; Computational Technique; Computational algorithm; Computer Assisted; Computers; Consumption; Detection; Diagnosis; Diagnostic; Disease; Fatigue; Fistula; Goals; Health; Hemorrhage; Human; Image; Individual; Informatics; Interobserver Variability; Intracranial Aneurysm; Knowledge; Location; Manuals; Measurement; Measures; Methods; Modeling; Morbidity - disease rate; Nervous System Trauma; Neurosurgeon; Operative Surgical Procedures; Outcome; Paralysed; Patients; Persons; Population; Positioning Attribute; Prevalence; Preventive treatment; Process; Reader; Research; Rotation; Ruptured Aneurysm; Scanning; Scheme; Sensitivity and Specificity; Shapes; Source Code; Speech; Stenosis; Stroke; Surface; Symptoms; Techniques; Time; Training; United States; Validation; Vasculitis; Vasospasm; Visualization; Work; X-Ray Computed Tomography; accurate diagnosis; cerebrovascular; clinical diagnosis; clinical imaging; computer aided detection; computerized; deep learning; deep learning model; design; disease diagnosis; image processing; imaging Segmentation; imaging modality; improved; malformation; mortality; neurosurgery; novel; open source; operation; shape analysis; statistics; tool

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.

该临床信息学项目的目标是开发计算技术来建模和分析大脑 用于检测作为脑血管疾病（CVD）标志的形态异常的血管。 该项目解决了神经放射学和神经外科领域的一个重要挑战：如何准确诊断 计算机断层扫描血管造影 (CTA) 上的 CVD。 CVD包括颅内动脉瘤、中风、颅内动脉瘤 血管狭窄、硬脑膜瘘和其他脑血管疾病，这些疾病具有严重的 后果，因为它们会导致出血、中风、神经损伤和死亡。事实上，每年，CVD 都会导致 美国有超过 10 万人死亡，更多人口遭受永久性损害，包括 中风、瘫痪和失语。如果我们能够更准确、更及时地诊断 CVD，死亡率和 发病率可显着降低。 脑成像是 CVD 的一线诊断，其图像特征是脑血管 异常。然而诊断非常具有挑战性，因为临床医生需要筛选、放大和缩小 并旋转大量图像来检查每条血管是否畸形，是否是 血管壁变窄或形成颅内动脉瘤。同样，神经外科医生需要 在手术前读取脑部扫描图以定位异常的位置。 我们该项目的具体目标是开发新颖的计算技术，包括深度学习 建模和分析血管以检测异常并突出显示其位置以供临床医生检查 更远。虽然计算机还不够先进，无法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、更快速地执行所需的复杂操作 形状分析和量化，例如识别血管的异常扩张或狭窄 检测血管壁上的突出物。满足临床医生的要求，让他们受益 计算机辅助异常检测的显着效果，一旦异常被标记，他们就可以 为了对潜在的 CVD 进行高度准确的诊断和分类，我们设计了一个信息学模型 方法作为分析 CTA 图像的计算机辅助工具。我们将对个体血管进行建模 3D 空间中的整个脉管系统。然后，从脉管系统出发，我们将开发并实施多 通道深度学习模型专注于形状分析以检测血管异常。最后， 异常情况将以 3D 颜色标记，以便临床医生做出更准确的诊断、计划 预防性治疗，并进行精准手术，以造福患者健康。