Arterial input function Independent Measures of Perfusion with Physics Driven Models

摘要 在美国，急性缺血性中风 (AIS) 每年影响大约 700,000 名患者。 [本杰明 EJ 2019，循环]虽然静脉溶栓剂的引入改善了患者的预后，但 机械血栓切除术有效治疗方案的开发显着改变了 AIS 患者的临床管理，特别是选择合适的患者进行干预时。的 当前的治疗选择方法利用患者特定数据，很大程度上依赖于定量神经影像 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 在美国相对普及，已成为治疗中风的主要方式 病人分类。 脑灌注成像对于评估缺血半暗带和梗塞核心至关重要 动脉内溶栓患者选择的精确性。通常采用动态 CT 灌注扫描 到达后使用碘造影剂进行 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后处理成灌注图像 指标，使用许多 FDA 批准的软件包。这些包本质上都依赖于类似的 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， 通常包括脑血流量（CBF）、脑血容量（CBV）、平均通过时间（MTT）和时间 至最大造影剂浓度 (Tmax)。然后软件包将阈值应用于 CBF 并 Tmax 映射以从 CBF 生成假定的缺血“核心”，并从 Tmax 生成“半影”。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 自动化 AIF 选择，或探索系统方法来生产局部 AIF 以改善灌注 测量。定义独立于 AIF 选择的灌注指标可以显着改善 中风灌注分析，并减少患者的辐射暴露。本研究的目的是评估物理 基于脑灌注模型，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 与临床相关并可预测卒中结果的灌注测量。

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.