High Performance Automated System for Analysis of Fast Cardiac SPECT

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例是由它造成的。心肌灌注单光子发射断层扫描（MPS）成像心肌灌注（血液输送到心肌）允许医生在心脏病发作前发现疾病，并预测每年数百万患者的风险。这目前受到视觉解释需求的限制，这是高度可变的，取决于医生的经验。该项目的长期目标是提高对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精度。该建议建立在我们之前在传统心肌MPS方面的工作基础上，并着重于通过新型高效扫描仪获得的快速，低辐射MPS成像（fast-MPS）。具体来说，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。该算法将包括通过训练相关解剖数据来更好地检测心肌，以及一种新的方法来绘制心肌每个位置的异常灌注概率；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，增强快速mps对心脏病的诊断；3)演示新算法在不需要时自动取消MPS扫描其余部分的临床应用。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，该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，关于休息扫描取消的计算机决定对患者是安全的。这将导致在MPS研究中广泛采用低剂量应激成像，这将减少患者暴露的辐射量，并允许显着节省医疗费用。它还将导致核心脏病学实践的范式转变，这将最终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。

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.