A dual-layer flat panel x-ray detector based on an engineered amorphous chalcogenide alloy for quantifying coronary artery calcium

基于工程非晶硫属化物合金的双层平板 X 射线探测器，用于量化冠状动脉钙

• 批准号: 10698174
• 负责人: Shiva Abbaszadeh
• 金额: $ 50.06万
• 依托单位: UNIVERSITY OF CALIFORNIA SANTA CRUZ
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-07 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10698174
• 关键词: Alloys; Arterial Fatty Streak; Arteries; Artificial Intelligence; Bone Density; Calcium; Cardiovascular Diseases; Cardiovascular system; Cessation of life; Charge; Clinical; Convection; Coronary Arteriosclerosis; Coupled; Data; Decision Making; Descriptor; Detection; Development; Disease; Early Diagnosis; Early Intervention; Engineering; Event; Future; Healthcare; Heart; Heart Diseases; Image; Imaging Techniques; Imaging technology; Location; Low Dose Radiation; Manufacturer; Medical Imaging; Modeling; Morbidity - disease rate; Morphologic artifacts; Motion; Motivation; Optics; Osteoporosis; Patient-Focused Outcomes; Patients; Performance; Physics; Population; Predictive Factor; Preventive healthcare; Probability; Property; Radiation; Research; Research Personnel; Resolution; Resource-limited setting; Roentgen Rays; Selenium; Structure; System; Technology; Testing; Thermal Conductivity; Thoracic Radiography; Time; Training; Tuberculosis; Vascular blood supply; X-Ray Computed Tomography; X-Ray Medical Imaging; artificial intelligence algorithm; automated algorithm; calcification; cardiovascular risk factor; commercialization; coronary artery calcification; coronary artery calcium; cost; density; design; detection sensitivity; detector; digital; early detection biomarkers; electric field; experience; heart imaging; heart motion; image guided; image processing; imager; improved; innovation; lung cancer screening; machine learning algorithm; mortality; novel; operation; population based; quantum; radiological imaging; respiratory; screening; segmentation algorithm; soft tissue; standard of care; success

PROJECT ABSTRACT Heart disease is extremely prevalent, with about one in every four deaths (in the US) being attributed to heart disease. Early detection of cardiovascular events, especially before patients become symptomatic, has immense impact in preventive healthcare, reducing the morbidity and mortality associated with cardiovascular disease. Coronary artery calcification (CAC), a strong predictor for future cardiovascular events, is a component of atherosclerotic plaque buildup in the arteries that supply blood to the heart, leading to coronary artery disease (CAD). Identification of CAC is clinically important because it is used for cardiovascular risk and therapy decision making. Currently, CAC is quantified by computed tomography (CT), however, CT-based population screening is not widely utilized due to cost and radiation burden. Chest x-rays (CXR) are the most common medical imaging procedure and have higher availability than CT in low-resource settings, lower radiation dose, and higher patient throughput that could be used for screening purposes. Unfortunately, due to the lack of quantification in CXR, only qualitative descriptors are possible. The objective of this proposal is therefore to bring much-needed quantification to CXR, particularly for detecting and quantifying CAC by combining a new dual-layer x-ray detector and artificial-intelligence based image processing. The proposed dual-layer detector utilizes alloys of amorphous selenium (a-Se) that achieve favorable electro-optical properties (e.g., higher charge carrier mobilities and higher gain) compared to conventional a-Se based x-ray detectors. This technology has four major components: (1) a top layer direct convection a-Se alloys on an imaging backplane, (2) a bottom layer indirect conversion a-Se alloy with intrinsic gain on an imaging backplane coupled to a scintillator, (3) top panel and bottom panel integration into a dual-layer detector, and (4) a machine learning algorithm that enhances accuracy of the quantitative information from the dual-layer detector. The detector development leverages a mature platform from Varex Imaging, a leading manufacturer of x-ray detectors. We expect to show that the proposed system has higher spatial resolution images and higher sensitivity to detect small, high contrast features (calcifications) and to separate materials such as calcium from soft tissue. This approach will allow accurate quantification of predictive factors and will have immense impact in proactive healthcare, improving the clinical outcomes of patients, and reducing the number of deaths associated with cardiovascular disease. While our focus is on CAC, we expect this technology to broadly improve CXR for early detection of lung cancer, tuberculosis, and other diseases such as osteoporosis via quantification of bone mineral density.

项目摘要 心脏病极为普遍，每四分之一（在美国）归因于心脏 疾病。早期发现心血管事件，尤其是在患者症状之前，有巨大 预防保健的影响，降低与心血管疾病相关的发病率和死亡率。 冠状动脉钙化（CAC）是未来心血管事件的有力预测因子，是 动脉粥样硬化的动脉斑块积聚，为心脏提供血液，导致冠状动脉疾病 （CAD）。 CAC的识别在临床上很重要，因为它用于心血管风险和治疗决策 制作。目前，CAC通过计算机断层扫描（CT）量化，但是，基于CT的人群筛查 由于成本和辐射负担，并未广泛使用。胸部X射线（CXR）是最常见的医学成像 在低资源设置，较低的辐射剂量和较高的患者中，步骤比CT更高 可用于筛选目的的吞吐量。不幸的是，由于CXR缺乏量化， 只有定性描述符。因此，该提议的目的是带来急需的 量化CXR，特别是用于通过组合新的双层X射线来检测和量化CAC 基于探测器和基于人造的图像处理。拟议的双层检测器利用 具有良好的电光特性的无定形硒（A-SE）（例如，较高的电荷载体 与常规的基于A-SE的X射线检测器相比，迁移率和更高的增益。这项技术有四个主要 组件：（1）顶层直接对流A-SE合金在成像背板上，（2）底层间接 转换A-SE合金具有固有增益在成像背板上的固有增益，耦合到闪烁器，（3）顶部面板和 底面板集成到双层检测器中，以及（4）一种增强准确性的机器学习算法 来自双层检测器的定量信息。检测器开发利用了成熟的 X射线检测器的领先制造商Varex Imaging的平台。我们希望表明提议 系统具有较高的空间分辨率图像和较高的灵敏度，以检测小的高对比度特征 （钙化）和将诸如钙之类的材料与软组织分开。这种方法将允许准确 量化预测因素，并将对积极的医疗保健产生巨大影响，从而改善临床 患者的结果，减少与心血管疾病有关的死亡人数。而我们的 重点是CAC，我们预计该技术将广泛改善CXR，以早日检测肺癌， 结核病和其他疾病（例如骨矿物质密度定量），例如骨质疏松症。