Automated Tumor Diagnosis with Contrast-Enhanced Ultrasound

描述(由申请人提供)：该项目的目标是开发一种计算系统，能够逐个像素地分析对比增强超声(CEUS)扫描图像，并量化肿瘤的血流特征，从而以高精度和可靠性半自动诊断肿瘤病变。在过去的十年里，CEUS通过静脉注射2？m气体填充的微泡显著提高了超声的诊断潜力，导致了灌流组织的增强，并允许对肿瘤血管和灌流进行高度特异的时间分辨成像。然而，CEUS影像分析需要经验丰富的放射科医生可靠而准确地诊断肿瘤，观察者之间的可靠性通常可以 一个问题。一个能够准确区分良性肿瘤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。这项拟议的研究利用了加州大学圣地亚哥分校其他经批准的临床研究中肝脏和乳房良性和恶性病变的两个CEUS数据集。为了取得临床上的成功，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会经历多个方向的运动。由于图像配准技术只能校正平面内的运动，我们将开发一种新的贯穿平面的运动滤波技术，将时间-强度曲线分析集中在运动周期内同一物理位置收集的数据点上。逐个像素的血流动力学和增强测量将定量和可靠地表征整个肿瘤的行为和异质性，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，对从数据集中随机选择的一半电影训练稀疏线性判别分析分类器。其余的电影将组成测试集，将评估该系统在正确区分测试集中的良性肿瘤和恶性肿瘤方面的准确性。准确诊断肿瘤的自动化系统将使临床医生能够在知情的情况下决定治疗患者的最佳行动方案。除了提供最佳的患者护理外，这些微创技术对患者的耐受性更强，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。通过消除对专家放射科医生手动解释CEU的需要，这一医学进步可以帮助改善全国范围内获得高质量医疗保健的机会，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。考虑到超声波系统相对较低的成本和全世界的接受度，这项拟议的研究的医疗影响远远超出了美国的国界，并有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.