Noninvasive Real-time Estimation of Cerebral Blood Flow for Personalized Stroke Assessment

用于个性化中风评估的脑血流量无创实时估计

• 批准号: 9898495
• 负责人: Hessam Babaee
• 金额: $ 7.89万
• 依托单位: UNIVERSITY OF ARIZONA
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2021-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9898495
• 关键词: 3-Dimensional; Address; Adoption; Age; Algorithms; American; Angiography; Biomedical Engineering; Blood Vessels; Blood flow; Brain; Brain region; Calibration; Cause of Death; Cerebrovascular Circulation; Cerebrovascular system; Cerebrum; Certification; Cessation of life; Characteristics; Classification; Clinical; Computer Models; Data Set; Databases; Development; Diabetes Mellitus; Diagnostic; Ensure; Gaussian model; Geometry; High Performance Computing; Hybrids; Hypertension; Image; Imaging Techniques; Infarction; Intuition; Ischemic Stroke; Machine Learning; Maps; Measurement; Measures; Medical Imaging; Modeling; Partner in relationship; Patient Care; Patients; Pattern; Performance; Perfusion; Physiology; Process; Property; Recording of previous events; Research; Risk Estimate; Scanning; Severities; Stroke; Techniques; Testing; Thrombus; Time; Tissue Viability; Tissues; Training; United States National Institutes of Health; Validation; X-Ray Computed Tomography; base; blood flow measurement; brain tissue; cerebral hemodynamics; clinical translation; cranium; disability; hemodynamics; imaging capabilities; improved; machine learning algorithm; mathematical methods; novel; parallel computer; perfusion imaging; simulation; stroke patient; tool; trend

PROJECT SUMMARY Stroke is the second leading cause of death and the third leading cause of disability worldwide. Similar trends exist in the U.S., where it is estimated that every 40 seconds a stroke occurs, leading to a stroke-related death every 4 minutes. Afflicting approximately 800,000 Americans each year, time sensitive and widely practicable diagnostic algorithms are paramount. Despite increasing emphasis on the significance of blood flow characteristics in pursuing strokes, estimating regional cerebral blood flow (rCBF) inside the skull is no trivial task. Imaging techniques that provide rCBF distribution may be unavailable due to time constraint or access to facilities, or simply the expenses attached to it. In our preliminary studies, we have developed a model that can give blood flow estimates in real-time for the entire brain vasculature. Here, we propose a novel hybrid computational model that utilizes high-performance computing and machine learning to simulate the entire brain vasculature, as well as provide real time measures of regional CBF. We will use novel mathematical methods to determine the vascular geometry of each subject using our existing imaging database that includes CTA and CBF images from stroke patients, and will subsequently validate our simulations against the same dataset. Finally, we will use machine learning algorithms to extract patterns in stroke patients' blood flow and give estimates for stroke severity predictors on patient- specific measurements.

项目总结

项目成果

Automatic segmentation, feature extraction and comparison of healthy and stroke cerebral vasculature.

• DOI: 10.1016/j.nicl.2021.102573
• 发表时间: 2021
• 期刊: NeuroImage. Clinical
• 作者: Deshpande A;Jamilpour N;Jiang B;Michel P;Eskandari A;Kidwell C;Wintermark M;Laksari K
• 通讯作者: Laksari K