Automated Tumor Diagnosis with Contrast-Enhanced Ultrasound

描述（由申请人提供）：本项目的目标是开发一种计算系统，该系统逐像素分析对比增强超声（CEUS）电影，并量化肿瘤灌注特征，以高准确性和可靠性半自动诊断肿瘤病变。在过去的十年中，CEUS通过静脉注射~2 μ m的充满气体的微泡，显著提高了超声的诊断潜力，从而增强了灌注组织，并允许对肿瘤血管和灌注进行高度特异性的时间分辨成像。然而，CEUS电影的分析需要非常有经验的放射科医生来可靠和准确地诊断肿瘤，并且观察者间的可靠性通常是不可靠的。 问题一个能够准确区分良性和恶性肿瘤的原型系统已经开发出来，在动物肿瘤模型上进行了测试，并发表了。拟议的研究利用了来自加州大学圣地亚哥分校其他批准的临床研究的肝脏和乳腺良性和恶性病变的两个CEUS数据集。为了实现临床成功，需要更复杂的运动校正算法，因为位于肺部附近的肿瘤在呼吸下经历多个方向的运动。由于图像配准技术只能纠正平面内运动，我们将开发一种新的通过平面运动滤波技术，将时间-强度曲线分析集中在运动周期内相同物理位置收集的数据点上。逐像素灌注动力学和增强测量将定量和可靠地表征整体肿瘤行为和异质性，并将形成区分肿瘤类型的基础。为了开发计算机辅助诊断系统，我们将通过最小化10倍交叉验证错误率，在从数据集中随机选择的一半电影上训练稀疏线性判别分析分类器。剩余的电影将形成测试集，并将评价系统在测试集中正确区分良性与恶性肿瘤的准确性。一个准确诊断肿瘤的自动化系统将使临床医生能够就治疗患者的最佳行动方案做出明智的决定。除了允许最佳的患者护理外，这些微创技术将更容易被患者耐受，将减少不必要的DNA损伤电离辐射暴露，并降低医疗保健系统的成本。通过消除专家放射科医生手动解释CEUS的需要，这种医学进步可以帮助改善全国范围内获得优质医疗保健的机会，特别是在服务不足的社区，那里可能没有经验丰富的放射科医生。考虑到超声系统相对较低的成本和全球范围内的接受度，这项拟议研究的医疗影响远远超出了美国的边界，并可能改善不太富裕国家的医疗保健。

DESCRIPTION (provided by applicant): The goal of this project is to develop a computational system that analyzes contrast-enhanced ultrasound (CEUS) cines on a pixel-by-pixel basis and quantifies tumor perfusion characteristics to semi-automatically diagnose tumor lesions with high accuracy and reliability. Over the past decade, CEUS has significantly improved ultrasound's diagnostic potential by intravenously injecting ~2 ¿m gas filled microbubbles, causing enhancement of perfused tissues and allowing highly specific time-resolved imaging of tumor vasculature and perfusion. However, analysis of CEUS cines requires highly experienced radiologists to reliably and accurately diagnose tumors, and interobserver reliability can often be an issue. A prototype system capable of accurately differentiating benign and malignant tumors has already been developed, tested on an animal tumor model, and published. The proposed research makes use of two CEUS datasets of benign and malignant lesions in the liver and breast from other approved clinical studies at UC San Diego. To achieve clinical success, more sophisticated motion correction algorithms are required because tumors located near the lungs experience motion in multiple directions under breathing. Since image registration techniques can only correct in-plane motion, we will develop a novel through-plane motion filtering technique to focus time-intensity curve analysis on data points collected at the same physical position within the motion cycle. Pixel-by-pixel perfusion kinetic and enhancement measurements will quantitatively and reliably characterize overall tumor behavior and heterogeneity and will form the basis for differentiating tumor types. To develop the computer-aided diagnosis system, we will train a sparse linear discriminant analysis classifier on half of the cines randomly selected from the dataset by minimizing the 10-fold cross-validation error rate. The remaining cines will form the testing set, and the system will be evaluated on its accuracy in correctly differentiating benign versus malignant tumors in the testing set. An automated system to accurately diagnose tumors will allow clinicians to make informed decisions on the best course of action to treat their patients. In addition to allowing optimal patient care, these minimally invasive techniques will be more tolerable to the patient, will reduce unnecessary exposure to DNA-damaging ionizing radiation, and reduce costs to the healthcare system. By eliminating the need for expert radiologists for manually interpretation of CEUS, this medical advancement can help improve access to quality healthcare nationwide, especially in underserved communities where highly experienced radiologists may not be available. Considering the relatively low cost and worldwide acceptance of ultrasound systems, the medical impact of this proposed research extends far beyond the borders of the United States and can potentially improve medical care in less affluent nations.