Arterial input function Independent Measures of Perfusion with Physics Driven Models

动脉输入功能 通过物理驱动模型独立测量灌注

• 批准号: 10353761
• 负责人: Yueh Z Lee
• 金额: $ 18.75万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-30 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10353761
• 关键词: Accident and Emergency department; Accounting; Acute; Affect; Assimilations; Blood Circulation; Blood flow; Bolus Infusion; Brain; Cerebrovascular Circulation; Cerebrum; Clinical; Clinical Management; Computer software; Contrast Media; Data; Decision Making; Dependence; Development; Diffusion; Evaluation; FDA approved; Goals; Image; Infarction; Institution; Intervention; Intravenous; Ischemic Penumbra; Ischemic Stroke; Magnetic Resonance Imaging; Maps; Measurement; Measures; Mechanics; Methods; Modality; Modeling; Motion; Movement; Noise; Pathway interactions; Patient Selection; Patient Triage; Patient-Focused Outcomes; Patients; Perfusion; Perfusion Weighted MRI; Physics; Process; Radiation exposure; Reperfusion Therapy; Risk; Scanning; Selection for Treatments; Shapes; Stroke; System; Techniques; Thrombectomy; Time; Tissues; Treatment Protocols; United States; X-Ray Computed Tomography; base; cerebral blood volume; clinical predictors; clinically relevant; effective therapy; fluid flow; improved; neuroimaging; perfusion imaging; post stroke; spatial relationship; stroke outcome; stroke patient; stroke therapy; thrombolysis

ABSTRACT Acute Ischemic Stroke (AIS) affects approximately 700,000 patients each year in the United States. [Benjamin EJ 2019, Circulation] Though the introduction of intravenous thrombolytics improved patient outcomes, the development of effective treatment regimens with mechanical thrombectomy has significantly altered the clinical management of AIS patients, especially when appropriate patients are selected for intervention. The current treatment selection approaches utilize patient specific data heavily relies on quantitative neuroimaging approaches, derived from either Computer Tomography (CT), or to a lesser extent magnetic resonance imaging (MRI). CT, with its relative availability within the US, has been the primary modality used for stroke patient triage. Brain perfusion imaging has been central to the evaluation of the ischemic penumbra and infarct core enabling precision in patient selection for intra-arterial thrombolysis. Typically dynamic CT perfusion scans with repeated scans 40 to 60 time points with the administration of iodinated contrast are obtained upon the arrival in the emergency room. These images are automatically or semi-automatically post-processed into perfusion metrics, using a number of FDA approved software packages. These packages all essentially rely on a similar post-processing pathway for the dynamically acquired images, consisting of motion correction, arterial input function selection and some form of deconvolution post-processing. A set of perfusion maps are generated, typically including cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT) and time to the maximum contrast concentration (Tmax). The software packages then apply thresholds to the CBF and Tmax maps to generate a presumed ischemic “core” from the CBF and “penumbra” from the Tmax. However, the dependence of these values on the arterial input function (AIF) selected has resulted in extensive efforts to automate AIF selection, or explore systematic methods to produce local AIFs to improve perfusion measurements. Defining a perfusion metric that is independent of AIF selection could substantially improve stroke perfusion analysis, and reduce patient radiation exposure. The goal of this study is to evaluate a physics based model of cerebral perfusion for evaluating perfusion parameters from CT perfusion modalities. The critical requirements of the new technique include independence from AIF selection, quantitative and stable measurements of perfusion that are clinically relevant and predictive of stroke outcomes.

抽象的 在美国，急性缺血性中风（AIS）每年影响约70万名患者。 [本杰明 EJ 2019，循环]，尽管引入了静脉注射溶栓的引入改善了患者的预后 通过机械血栓切除术开发有效的治疗方案已显着改变 AIS患者的临床管理，尤其是在选择合适的患者进行干预时。 当前的治疗选择方法利用患者的特定数据在很大程度上依赖于定量神经影像学 方法，源自计算机断层扫描（CT）或较小程度的磁共振 成像（MRI）。 CT及其在美国境内的相对可用性一直是中风的主要方式 患者分类。 脑灌注成像是评估缺血性半阴茎和基础设施的核心 患者选择动脉内溶栓的精度。通常使用动态CT灌注扫描 到达时，可以重复扫描40至60个时间点，并给予碘化对比度 在急诊室。这些图像是自动或半自动的，后加工为灌注 指标，使用许多FDA批准的软件包。这些包装本质上都取决于类似的 动态获得的图像的后处理途径，包括运动校正，动脉输入 功能选择和某种形式的反卷积后处理。产生了一组灌注图， 通常包括脑血流（CBF），脑血体积（CBV），平均转运时间（MTT）和时间 达到最大对比度浓度（TMAX）。然后，软件包将阈值应用于CBF，并且 TMAX映射以从CBF和TMAX产生预览缺血“核心”和“ Penumbra”。然而， 这些值对所选动脉输入函数（AIF）的依赖性导致了广泛的努力 自动选择AIF或探索系统的方法来生产局部AIF以改善灌注 测量。定义独立于AIF选择的灌注指标可以大大改善 中风灌注分析，并减少患者辐射暴露。这项研究的目的是评估物理 基于CT灌注方式评估灌注参数的脑灌注模型。这 新技术的关键要求包括独立于AIF选择，定量和稳定 在临床上相关且预测中风结果的灌注测量。