High Performance Automated System for Analysis of Fast Cardiac SPECT

描述(申请人提供)：冠状动脉疾病仍然是世界范围内的一个主要公共卫生问题。在美国，大约每6人中就有1人死于此病。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，医生可以在心脏病发作前检测疾病，并预测每年数百万患者的风险。这目前受到视觉解释的需要的限制，视觉解释是高度可变的，取决于医生的经验。该计划的长期目标是改进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。这项建议建立在我们之前在传统心肌MPS上的工作基础上，并专注于通过新的高效扫描仪获得的快速、低辐射MPS成像(FAST-MPS)。具体地说，我们的目标是：1)开发新的图像处理算法，用于FAST-MPS的全自动分析。这些算法将包括通过使用相关的解剖学数据进行训练来更好地检测心肌，以及一种新的方法来绘制心肌每个位置的异常灌注概率图；2)通过整合临床数据、负荷测试参数和定量图像特征的机器学习算法，增强快速MPS扫描对心脏病的诊断；3)展示新算法的临床实用性，这些算法应用于在不需要时自动取消MPS扫描的其余部分。在检测阻塞性冠状动脉疾病方面，新系统将比临床专家分析更准确。通过立即指示压力扫描是否正常，系统将允许在不需要休息成像部分时自动取消该部分(估计在所有MPS研究中超过60%)。我们的研究将证明，从诊断和预后的角度来看，关于取消静息扫描的计算机决策对患者是安全的。这将导致在MPS研究中广泛采用低剂量仅限压力成像，这将减少患者暴露在辐射中的量，并允许显著节省医疗保健。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而死亡的人数。

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.