Cardiac photon counting CT and its application in studying interactions between Alzheimer's and heart disease

心脏光子计数CT及其在研究阿尔茨海默病与心脏病相互作用中的应用

• 批准号: 10094804
• 负责人: CRISTIAN T BADEA
• 金额: $ 187.31万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-15 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10094804
• 关键词: 3-Dimensional; Address; Adoption; Affect; Aging; Algorithms; Alleles; Alzheimer' s Disease; Alzheimer' s disease model; Alzheimer' s disease risk; Animal Experiments; Animal Model; Apolipoprotein E; Arterial Fatty Streak; Atherosclerosis; Base of the Brain; Behavior; Behavior assessment; Biological Markers; Brain; Cardiac; Cardiovascular Diseases; Cardiovascular system; Cause of Death; Characteristics; Clinical; Cognitive; Data; Development; Diet; Dimensions; Disease Marker; Dose; Environmental Impact; Epidemiology; Exercise; Exposure to; Fatty acid glycerol esters; Foundations; Generations; Genes; Genetic; Genetic Models; Genetic Risk; Genotype; Heart Diseases; Human; Image; Imaging Device; Immune response; Impaired cognition; Intervention; Link; Magnetic Resonance; Magnetic Resonance Imaging; Malignant Neoplasms; Measurement; Measures; Methodology; Methods; Modeling; Monitor; Morphologic artifacts; Mouse Strains; Mus; Myocardial perfusion; NOS2A gene; Names; Nitric Oxide Synthase; Noise; Outcome; Oxidation-Reduction; Pathologic; Performance; Phenotype; Photons; Physiologic Monitoring; Pre-Clinical Model; Process; Protein Isoforms; Protocols documentation; Research; Resolution; Risk; Risk Factors; Role; Scanning; System; Technology; Testing; Time; X-Ray Computed Tomography; base; behavioral study; cardiac plaque; cardiovascular risk factor; combat; computerized data processing; data acquisition; deep learning; detector; environmental stressor; exercise intervention; exercise regimen; heart function; heart imaging; human subject; imaging modality; improved; in vivo; insight; learning strategy; magnetic resonance imaging biomarker; mouse model; non-invasive imaging; novel; photon-counting detector; pre-clinical; preclinical imaging; preclinical study; predictive modeling; prototype; reconstruction; resilience; response; sedentary lifestyle; simulation; spectral distortion; sugar; tool

PROJECT SUMMARY/ABSTRACT Aging is accompanied by increasing vulnerability to cardiovascular disease (CVD) and Alzheimer’s disease (AD). The ongoing rise in both AD and CVD has been ascribed to the increasing adoption of a Western sedentary lifestyle accompanied by a diet rich in fats and sugars. To understand the links between AD and CVD in human subjects, non-invasive imaging methods such X-ray computed tomography (CT) and magnetic resonance (MR) are essential. Cardiac CT is one of the most powerful applications of these methodologies at both clinical and preclinical levels, but it is currently limited by its low contrast resolution. Our primary objective in this proposal is to improve the current status of cardiac CT based on photon counting detector technology and demonstrate its capabilities in preclinical studies focused on studying the interaction between CVD and AD. Our central hypothesis is that cardiac photon counting CT will provide low dose spectral characterization of atherosclerotic plaques together with cardiac function, while enabling longitudinal monitoring of interventions such as exercise. We will pursue three specific aims. In specific aim 1, we will develop the theoretical foundation and GPU optimized tools for reconstruction of cardiac 5D (3D + Time + Energy) photon counting CT data. We will incorporate deep learning solutions to overcome fundamental barriers to the advancement of this technology: regularization to deal with image noise associated with photon binning, robust material decomposition to combat spectral distortion, and automated cardiac function and plaque analysis to handle data dimensionality. During the second specific aim, we will characterize the performance of our novel cardiac photon counting CT imaging using simulations, phantoms and animal experiments to show its benefits for atherosclerotic plaque characterization and cardiac function estimation. Finally, in specific aim 3 we will investigate if cardiovascular risk impacts brain phenotypes in animal models of genetic risk for AD. CVD and AD share a genetic link via the ApoE gene and its isomorphic allele 4 (APOE4). We will use APOE3/HN and APOE4/HN mouse strains that express the corresponding specific targeted-replacement human APOE allele, on a humanized Nitric Oxide Synthase 2 (denoted here as HN) background. Using these models, we will first assess the impact of a high fat, high sugar diet on cardiovascular phenotypes (atherosclerotic plaque size, numbers; cardiac function measured with CT) and how these genetic differences are reflected in behavior and brain MR based biomarkers compared with control mice in the same background. Finally, we will also investigate the potential to rescue these phenotypes using exercise as the intervention. The impact of the proposed research will validate the usage of photon counting CT technology to enhance routine cardiac CT imaging applications. Our project will enable new powerful integrative approaches to examine the impact of environmental stressors to alter APOE genotype- specific vulnerability, or resilience to CVD and AD.

项目概要/摘要 衰老伴随着心血管疾病（CVD）和阿尔茨海默病（AD）的增加。 AD 和 CVD 发病率的持续上升归因于西方久坐习惯的日益普及 生活方式伴随着富含脂肪和糖的饮食。了解人类 AD 和 CVD 之间的联系 受试者、非侵入性成像方法，例如 X 射线计算机断层扫描 (CT) 和磁共振 (MR) 是必不可少的。心脏 CT 是这些方法在临床和临床上最强大的应用之一。 临床前水平，但目前受到其低对比度分辨率的限制。我们此提案的主要目标 是为了改善基于光子计数探测器技术的心脏CT现状并展示 其临床前研究能力侧重于研究 CVD 和 AD 之间的相互作用。我们的中央 假设心脏光子计数 CT 将提供动脉粥样硬化的低剂量光谱特征 斑块和心脏功能，同时能够纵向监测运动等干预措施。 我们将追求三个具体目标。在具体目标1中，我们将发展理论基础和GPU 用于重建心脏 5D（3D + 时间 + 能量）光子计数 CT 数据的优化工具。我们将 结合深度学习解决方案来克服该技术进步的基本障碍： 正则化处理与光子分级相关的图像噪声，稳健的材料分解以对抗 光谱失真，以及自动心脏功能和斑块分析来处理数据维度。期间 第二个具体目标是，我们将表征新型心脏光子计数 CT 成像的性能 使用模拟、模型和动物实验来展示其对动脉粥样硬化斑块的益处 表征和心脏功能估计。最后，在具体目标 3 中，我们将研究心血管是否 风险影响 AD 遗传风险动物模型中的大脑表型。 CVD 和 AD 通过以下方式共享遗传联系： ApoE 基因及其同构等位基因 4 (APOE4)。我们将使用 APOE3/HN 和 APOE4/HN 小鼠品系 在人源化一氧化氮上表达相应的特异性靶向替代人类 APOE 等位基因 合成酶 2（此处表示为 HN）背景。使用这些模型，我们将首先评估高脂肪的影响， 高糖饮食对心血管表型的影响（动脉粥样硬化斑块的大小、数量；测量的心脏功能 与 CT）以及这些遗传差异如何反映在行为和基于大脑 MR 的生物标志物中进行比较 与处于相同背景的对照小鼠。最后，我们还将调查拯救这些的潜力 使用运动作为干预的表型。拟议研究的影响将验证 光子计数 CT 技术增强常规心脏 CT 成像应用。我们的项目将启用新的 强大的综合方法来检查环境压力因素对改变 APOE 基因型的影响 针对 CVD 和 AD 的特定脆弱性或恢复能力。