High Performance Automated System for Analysis of Fast Cardiac SPECT

用于快速心脏 SPECT 分析的高性能自动化系统

• 批准号: 8906912
• 负责人: Piotr J Slomka
• 金额: $ 68.5万
• 依托单位: CEDARS-SINAI MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-07-18 至 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8906912
• 关键词: Adoption; Algorithms; Blood; Blood flow; Cardiology; Cessation of life; Clinical; Clinical Data; Computer Systems; Computers; Coronary Arteriosclerosis; Coronary heart disease; Data; Detection; Diagnosis; Diagnostic; Disease; Dose; Generations; Health; Healthcare; Heart Diseases; Image; Imaging Techniques; Intervention; Lead; Location; Machine Learning; Maps; Myocardial; Myocardial Infarction; Myocardial perfusion; Myocardium; Nuclear; Patient Selection; Patients; Performance; Perfusion; Photons; Physicians; Probability; Public Health; Radiation; Research; Research Personnel; Rest; Risk; Savings; Scanning; Stress; Stress Tests; System; Systems Analysis; Training; United States; Visual; Work; cardiac single photon emission computed tomography; experience; heart imaging; image processing; improved; novel; novel strategies; prognostic; programs; quantitative imaging; tomography; tool

DESCRIPTION (provided by applicant): Coronary artery disease remains a major public health problem worldwide. It causes approximately 1 of every 6 deaths in the United States. Imaging of myocardial perfusion (delivery of blood to the heart muscle) by myocardial perfusion single photon emission tomography (MPS) allows physicians to detect disease before a heart attack, and predict risk in millions of patients annually. This is currently limited by the need fo visual interpretation, which is highly variable and depends on the physician's experience. The long-term objective of this program is to improve the interpretation of this widely used heart imaging technique-achieving higher accuracy for disease detection than it is possible by the best attainable visual analysis. This proposal builds on our prior work in conventional myocardial MPS, and focuses on fast, low-radiation MPS imaging (fast-MPS) obtained by new high-efficiency scanners. Specifically, we aim to: 1) develop new image processing algorithms for a fully automated analysis of fast-MPS. The algorithms will include better heart muscle detection by training with correlated anatomical data and a novel approach for mapping the probability of abnormal perfusion for each location of the heart muscle; 2) enhance the diagnosis of heart disease from fast-MPS by machine- learning algorithms that integrate clinical data, stress test parameters, and quantitative image features; 3) demonstrate the clinical utility of the new algorithms applied to automatic canceling of the rest portion of the MPS scan, when not needed. The new system will be more accurate than the clinical expert analysis in the detection of obstructive coronary disease. By immediately indicating whether a stress scan is normal, the system will allow for the automatic cancellation of the rest imaging portion when it is not needed (estimated in over 60% of all MPS studies). Our research will demonstrate that the computer decision regarding rest-scan cancellation is safe for the patient, both from a diagnostic and prognostic standpoint. This will lead to a wide adoption of low-dose stress-only imaging for MPS studies, which would reduce the amount of radiation that patients are exposed to, and allow for significant healthcare savings. It will additionally lead to a paradigm shift in the practice of nuclear cardiology, which will ultimately result in better selection of patients who need intervention, and reduce the number of deaths due coronary artery disease.

描述（由申请人提供）：冠状动脉疾病仍然是全球主要的公共卫生问题。在美国，每6例死亡中就有1例是由它引起的。通过心肌灌注单光子发射断层扫描（MPS）对心肌灌注（将血液输送到心肌）进行成像，使医生能够在心脏病发作前检测疾病，并预测每年数百万患者的风险。目前，这是有限的需要视觉解释，这是高度可变的，并取决于医生的经验。该计划的长期目标是改善这种广泛使用的心脏成像技术的解释，实现比最佳视觉分析更高的疾病检测准确性。这项建议建立在我们以前的工作，在传统的心肌MPS，并专注于快速，低辐射MPS成像（快速MPS）获得新的高效扫描仪。具体而言，我们的目标是：1）开发新的图像处理算法，用于快速MPS的全自动分析。这些算法将包括通过使用相关解剖数据进行训练来更好地检测心肌，以及用于映射心肌每个位置的异常灌注概率的新方法; 2）通过整合临床数据、压力测试参数和定量图像特征的机器学习算法来增强对来自快速MPS的心脏病的诊断; 3）证明在不需要时自动取消MPS扫描其余部分的新算法的临床效用。新系统在检测阻塞性冠状动脉疾病方面将比临床专家分析更准确。通过立即指示应力扫描是否正常，系统将允许在不需要时自动取消其余成像部分（估计超过60%的MPS研究）。我们的研究将证明，从诊断和预后的角度来看，计算机决定取消休息扫描对病人是安全的。这将导致MPS研究广泛采用低剂量应力成像，这将减少患者暴露的辐射量，并节省大量医疗费用。它还将导致核心脏病学实践的范式转变，最终将导致更好地选择需要干预的患者，并减少因冠状动脉疾病死亡的人数。