AI-based Cardiac CT

基于人工智能的心脏CT

• 批准号: 10654259
• 负责人: Hengyong Yu
• 金额: $ 65.34万
• 依托单位: UNIVERSITY OF MASSACHUSETTS LOWELL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10654259
• 关键词: 3-Dimensional; Academic Medical Centers; Acceleration; Address; Adrenergic beta-Antagonists; Adult; Algorithms; Aorta; Area; Arrhythmia; Arteries; Artificial Intelligence; Atrial Fibrillation; Breathing; Cardiac; Cardiovascular Diseases; Cessation of life; Clinical; Clinical Research; Compensation; Computer software; Coronary Angiography; Coronary artery; Data; Data Set; Dedications; Development; Diagnosis; Dimensions; Elderly; Electrocardiogram; Evaluation; Exhibits; Freezing; Goals; Guidelines; Healthcare; Heart; Heart Rate; Human; Hybrids; Image; Incidence; Individual; Knowledge; Left; Location; Maps; Medical; Methods; Modality; Modeling; Modernization; Morbidity - disease rate; Morphologic artifacts; Motion; Myocardial Infarction; Organ; Pathology; Patients; Performance; Pharmaceutical Preparations; Phase; Physiology; Positron-Emission Tomography; Prevalence; Qualitative Methods; Radiation exposure; Radon; Recommendation; Resolution; Rotation; Running; Scanning; Severities; Source; Stenosis; Structure; System; Tachycardia; Technology; Time; Training; Ventricular; X-Ray Computed Tomography; clinical application; clinically relevant; computer framework; contrast enhanced computed tomography; coronary computed tomography angiography; deep learning; design; detector; diagnostic value; driving force; empowerment; experience; experimental study; frontier; generative adversarial network; heart imaging; heart motion; image reconstruction; imaging approach; improved; large datasets; learning network; millisecond; mortality; next generation; open source; performance tests; practical application; reconstruction; signal processing; simulation; single photon emission computed tomography; success; temporal measurement; tomography; tool

Abstract Cardiovascular diseases (CVDs) are the leading cause of morbidity and mortality, with 19 million deaths globally in 2020 alone. Central to management of CVDs is deepening our knowledge of physiology and pathology of the heart. Major barriers to our greater understanding of the heart include its deep location and fast dynamics. Evidence from invasive coronary angiography indicates that the maximum velocity of cardiac structures is 52.5 mm/s, requiring a scan time of 19.1ms to eliminate motion artifacts. To achieve this temporal resolution with CT is extremely challenging. There have been substantial gains in CT hardware and software over the last decades (whole heart, dual-source, dedicated cardiac CT, and various approaches to cardiac motion compensation with ECG-gating) that have transformed coronary CT angiography into a robust and viable clinical tool. However, owing to the 140ms temporal resolution of current whole heart CT scanners, diagnosis is still challenging in patients who have irregular and/or fast heart rates especially in cases of arrhythmias and tachycardia, which commonly occur in older adults, many of whom exhibit atrial fibrillation. Here we will apply deep learning to radically improve cardiac CT reconstruction by attaining significantly higher spatial resolution, lower radiation exposure, and better image quality on both modern and legacy CT hardware. To improve wide-area-detector cardiac CT performance, we will develop a limited-angle reconstruction algorithm in the Analytic, Compressive, Iterative, and Deep (ACID) reconstruction framework that integrates a deep network trained on large data, sparsity-promotion, analytic modeling, and iterative refinement. For the first time, two of the preeminent advances in signal processing, compressive sensing and deep learning, will be combined to extract full information from scan data and image priors to freeze the beating heart. The specific aims H3 are: (1) Hyper Dataset: Projection datasets in the Radon space and the corresponding ground-truth images without motion artifacts in the image space will be generated in simulation, experiments, and clinical studies; (2) Hybrid Algorithm: A deep learning network and CS-module will be developed, integrated, and accelerated within the ACID framework for limited-angle free-breathing cardiac CT reconstruction, which will be shared on an open-source platform; and (3) Holistic Evaluation: The performance of our reconstruction software will be characterized, the stability and generalizability will be investigated, and task-based clinical applications will be demonstrated, including quantification of stenosis severity, aorta dimensions, and motion artifacts within the clinical setting of individuals with atrial fibrillation, tachycardia, and irregular heart rates. Completion of this project will yield a free-breathing cardiac CT algorithm with the unprecedented temporal resolution of 60ms, a 230% improvement over the state-of-the-art, allowing cardiac CT without clinically-relevant motion artifacts. This represents a major step towards the integration of model-driven and data-driven methods for CT image reconstruction, with a lasting impact on not only CT but also other tomographic modalities.

抽象的 心血管疾病（CVD）是发病率和死亡率的主要原因，全球1900万人死亡 仅在2020年。 CVD管理的核心正在加深我们对生理学和病理的了解 心。我们对心脏的更深入的主要障碍包括其深层的位置和快速的动态。 侵入性冠状动脉血管造影的证据表明，心脏结构的最大速度为52.5 mm/s，需要扫描时间为19.1ms才能消除运动伪影。通过 CT非常挑战。最后，CT硬件和软件在最后一个 数十年（全心全意，双源，专用心脏CT以及各种心脏运动方法 通过将冠状动脉血管造影转化为稳健且可行的临床的ECG Gating）的补偿 工具。但是，由于当前心脏CT扫描仪的140ms临​​时分辨率，诊断仍在 在心律不齐和/或快速心律的患者中，挑战性挑战 心动过速通常发生在老年人中，其中许多人暴露了房颤。 在这里，我们将深入学习，从而从根本上提高心脏CT重建，从 空间分辨率，较低的辐射曝光以及现代和传统CT硬件的图像质量更好。 为了改善广阔区域的心脏CT性能，我们将开发有限角度重建算法 在分析，压缩，迭代和深（酸）重建框架中，该框架整合了深度 对大型数据，稀疏促销，分析建模和迭代改进的网络进行了培训。首次， 信号处理，压缩灵敏度和深度学习的两个杰出进步将结合在一起 从扫描数据和图像先验中提取完整信息以冻结跳动的心脏。 特定目的H3是：（1）Hyper DataSet：ra空间中的投影数据集和相应的 图像空间中没有运动伪影的地面真相图像将在模拟，实验， 和临床研究； （2）混合算法：将开发，集成，深入学习网络和CS模块 并在酸框架内加速有限角度自由呼吸心脏CT重建，这将 在开源平台上共享； （3）整体评估：我们重建的表现 软件将被表征，稳定性和可推广性以及基于任务的临床能力 将展示应用，包括狭窄严重程度，主动脉维度和运动的定量 心房颤动，心动过速和心率不规则的个体临床环境中的伪影。 该项目的完成将产生一种自由呼吸的心脏CT算法，并具有前所未有的临时性 分辨率为60ms，比最先进的230％提高，允许心脏CT无临床相关的心脏CT 运动工件。这是朝着模型驱动和数据驱动方法集成的重大步骤 对于CT图像重建，不仅对CT，而且对其他断层扫描方式产生持久影响。