Arterial input function Independent Measures of Perfusion with Physics Driven Models

摘要 急性缺血性卒中（AIS）在美国每年影响约70万患者。[本杰明 EJ 2019，循环]尽管静脉溶栓的引入改善了患者结局， 采用机械血栓切除术的有效治疗方案的发展显著改变了 AIS患者的临床管理，特别是在选择适当的患者进行干预时。的 目前的治疗选择方法利用患者特异性数据， 方法，来自计算机断层扫描（CT），或在较小程度上来自磁共振 成像（MRI）。CT在美国的相对可用性，一直是用于卒中的主要方式 病人分诊 脑灌注成像已成为评价缺血半暗带和梗死核心的核心， 动脉内溶栓患者选择的精确性。典型的动态CT灌注扫描， 在到达时获得40至60个时间点的重复扫描 在急诊室这些图像被自动或半自动地后处理成灌注 使用许多FDA批准的软件包。这些软件包基本上都依赖于一个类似的 动态采集图像的后处理路径，包括运动校正、动脉输入 函数选择和某种形式的反卷积后处理。生成一组灌注图， 典型地包括脑血流量（CBF）、脑血容量（CBV）、平均通过时间（MTT）和时间 最大对比浓度（Tmax）。然后，软件包将阈值应用于CBF， Tmax映射以从CBF生成假定的缺血性"核心"和从Tmax生成"半暗带"。然而，在这方面， 这些值对所选择的动脉输入函数（AIF）的依赖性导致了广泛的努力， 自动化AIF选择，或探索系统方法来产生局部AIF以改善灌注 测量.定义一个独立于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一个物理 的脑灌注模型，用于评估CT灌注模式的灌注参数。的 新技术的关键要求包括独立于AIF选择，定量和稳定 灌注测量具有临床相关性并可预测卒中结局。

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.