NSF/FDA SIR: Numerical Model Observer for Multiple Abnormalities

主要研究者：Markey，Mia K.提案：1445713标题：针对多种异常的数值模型观测器意义所提出的模型观测器将使医学成像系统能够更快速地优化，特别是在技术开发的早期阶段。基本的范例是通过其预期临床任务的有效性来量化医学图像的质量。这一方面与FDA的使命一致，即促进新的、安全的和有效的医疗器械。更重要的是，Mia K. Markey和她在德克萨斯大学奥斯汀分校的研究生以及FDA成像和应用数学部门的研究人员，特别是Subok Park博士。除了通过该项目支持的研究生的经验整合研究和教育外，PI（Markey）预计这项工作将告知她的教学，使她可以改善生物医学工程课程在得克萨斯大学奥斯汀分校的主题，特别是有关成像和应用数学在FDA的部门。她可以改善生物医学工程课程在得克萨斯大学奥斯汀分校的主题是特别相关的成像和应用数学在FDA的部门。技术说明拟议的合作研究旨在提供一个结构化的机会，为研究生进行工程和科学研究在FDA的重点是改善设计的数值模型观察员的医疗成像研究。作为人类观察者的替代者，数值模型观察者为评估医学图像质量提供了一种客观的方法，因此，在成像设备的优化和评估中发挥着重要作用。FDA需要模范观察员来制定监管标准，并确定成像设备制造商必须解决的科学问题。在临床实践中，可能存在放射科医师需要在医学图像上发现的零个、一个或多于一个异常。所提出的研究的目的是开发一个模型观测器来表征在医学图像中的多个异常的检测，并估计异常的属性。这对于增强模型观测器的通用性及其在医学成像中的实际应用是至关重要的，因为当前最先进的模型观测器无法处理单个图像中的多个异常。该模型的主要思想是计算可疑位置集合的可能性，估计每个检测到的信号的参数，然后将所有信息组合成一个最终分数，揭示最可疑的场景。

PI: Markey, Mia K.Proposal: 1445713Title: Numerical Model Observer for Multiple AbnormalitiesSignificanceThe proposed model observer would enable more rapid optimization of medical imaging systems, especially during the early stages of the technology development. The underlying paradigm is to quantify the quality of a medical image by its effectiveness with respect to its intended clinical task. This aspect of is aligned with the mission of the FDA to facilitate new, safe, and effective medical devices. Of further importance is the development of a new research collaboration between Prof. Mia K. Markey and her graduate students at The University of Texas at Austin and researchers in the Division of Imaging and Applied Mathematics at the FDA, especially Dr. Subok Park. In addition to integrating research and education through the experiences of the graduate student supported by this project, the PI (Markey) anticipates that this work will inform her teaching such that she can improve biomedical engineering course offerings at The University of Texas at Austin with respect to topics that are particularly relevant to the Division of Imaging and Applied Mathematics at the FDA.that this work will inform her teaching such that she can improve biomedical engineering course offerings at The University of Texas at Austin with respect to topics that are particularly relevant to the Division of Imaging and Applied Mathematics at the FDA.Technical DescriptionThe proposed collaborative research is designed to provide a structured opportunity for a graduate student to conduct engineering and scientific research at the FDA focused on improving the design of numerical model observers for medical imaging research. As a surrogate of human observers, numerical model observers provide an objective method for assessing medical image quality, and, thus, play an important role in the optimization and assessment of imaging devices. The FDA needs model observers in order to develop regulatory criteria and identify scientific issues that imaging device manufacturers must address. In clinical practice, there may be zero, one, or more than one abnormality that a radiologist needs to find on a medical image. The objective of the proposed study is to develop a model observer to characterize the detection of multiple abnormalities in a medical image and to estimate the properties of the abnormalities. This is critical for enhancing the generality of model observers and their practical application in medical imaging since current state-of-art model observers are unable to handle multiple abnormalities in a single image. The main idea of the proposed model is to compute the likelihood of an ensemble of suspicious locations, estimate the parameters of each detected signal, and then assemble all the information into a final score that reveals the most suspicious scenario.