SBIR Phase I: Improving Accuracy and Reducing Scan Time of Dynamic Brain PET

这个SBIR第一阶段项目将产生一种动态正电子发射断层扫描(PET)的定量数据处理技术。动态PET与静态PET的不同之处在于它收集随时间变化的示踪剂浓度数据。这是一种高度灵敏和准确的蛋白质靶标功能和分子成像方法，例如阿尔茨海默病、帕金森病或脑创伤的生物标记物。然而，动态正电子发射计算机断层扫描成像时间长、处理复杂，临床应用较少。这项提案中将要开发的技术将缩短所需的成像时间并简化分析，从而允许对疾病生物标志物进行准确和可靠的成像，以帮助药物开发、临床诊断和治疗监测。建议的分析工作流程将作为基于云的服务提供，客户可以使用该服务来分析新的或历史的PET数据集。为了克服依赖不太准确和信息量较少的静态PET成像的既定做法，赠款获得者计划使用专门的物理模型和过去获取的人体受试者图像对新技术进行彻底验证。这项新技术预计将在医学研究和药物开发方面产生最大影响。公认的分析动态PET数据集的实践涉及重建描述示踪剂生物化学的隐含隔室模型的参数，这涉及到几个精度降低的近似，并且需要长时间的采集。本项目中提出的替代方法依赖于使用几种处理技术的组合来分解动态PET图像序列，这些处理技术结合了示踪剂药代动力学的基本特征，而不需要对模型参数或潜在的解剖结构做出明确的假设。提出的方法的关键部分是动态结构的因子分析，这是一种非负矩阵分解技术，过去在放射性核素图像分析中得到了成功的应用。该工作流程的结果是显示放射性示踪剂的特异性结合的组织的3D集中。为了验证该方法的定量准确性，研究人员将建立一个专用的物理模型并对其进行成像，该模型能够产生具有不同示踪动力学的重叠分布。为了验证减少扫描时间的声明，新的工作流程将使用越来越短的输入时间窗口来分析现有的动态PET人类受试者数据集。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This SBIR Phase I Project will productize a quantitative data processing technology in dynamic positron emission tomography (PET). Dynamic PET's distinction from its static counterpart is in the fact that it collects time-dependent tracer concentration data. It is a highly sensitive and accurate way of functional and molecular imaging of protein targets such as biomarkers of Alzheimer's disease, Parkinson's disease, or brain trauma. Unfortunately, dynamic PET is rarely used in clinic because of long imaging time and complex processing. The technology to be developed in this proposal will shorten the required imaging time and simplify the analysis, allowing for accurate and reliable imaging of disease biomarkers for aiding drug development, clinical diagnostics, and treatment monitoring. The proposed analytic workflow will be offered as a cloud-based service that can be used by the customers to analyze new or historical PET datasets. In order to overcome the established practice of relying on less accurate and informative static PET imaging, the grant recipients plan to conduct a thorough validation of the new technology using a dedicated physical phantom and human subject images acquired in the past. The largest impact of the new technology is expected in medical research and drug development. The accepted practice of analyzing dynamic PET datasets involves reconstruction of the parameters of implied compartment model describing the tracer biochemistry, which involves several accuracy-degrading approximations and requires lengthy acquisition. The alternative approach proposed in this project relies on decomposing the dynamic PET image sequences using a combination of several processing techniques that incorporate the essential features of tracer pharmacokinetics without making explicit assumptions about the model parameters or about the underlying anatomy. The key part of the proposed methodology is factor analysis of dynamic structures, a non-negative matrix decomposition technique with successful past applications in radionuclide image analysis. The result of the workflow is a 3D concentration of tissues that exhibit specific-binding of the radiotracer. In order to validate the quantitative accuracy of the approach, the researchers will build and image a dedicated physical phantom capable of producing overlapping distributions with different tracer dynamics. In order to validate the scan-time reduction claims, the new workflow will be used to analyze existing dynamic PET human subject datasets using increasingly shorter input time windows.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.