INCREASING CLINICAL ACCESS BY REDUCING SCAN TIME OF DYNAMIC NUCLEAR CARDIAC IMAGING WITH SUPERIOR DIAGNOSIS

NIH项目摘要（最多30行） 该计划旨在改善临床上冠状动脉疾病（CAD）的诊断和预后 通过使动态核成像能够被广泛使用。该协议目前主要 在选定的研究设施中，长期以来已知其对于定量心脏灌注成像是上级的。然而， 动态核成像在临床诊断中几乎没有渗透。原因有几个方面 技术上的挑战，最引人注目的是其不合理的长扫描时间。我们的主要 一种假设是，可以使用独特的诊断参数，如心肌血流量和冠状动脉血流储备， 通过动态成像采集在比目前实践的扫描时间短得多的扫描时间内获得。 减少扫描时间将消除将动态核成像转化为临床的主要障碍。 我们的目标将通过两种核成像模式的创新算法开发来实现： 单光子发射计算机断层扫描（SPECT）和正电子发射断层扫描（PET）。先前 我们已经开发出基于一种称为非负矩阵的新型数学工具的尖端算法 因子分解（NMF）用于这两种模式。在这个项目中，我们将大大推进这些算法 朝着我们的目标，同时也提高了估计诊断有价值的信息的准确性。为 为此，我们将开发额外的高级数学组件，如基于拓扑的一维分析 时间序列数据和器官的解剖形状，以指导数据分析中的解决方案。我们将验证我们的 PET中放射性示踪剂13 NH3和99 Tc-替曲膦的真实数值模型结果和临床数据 在SPECT中。我们将从加州大学的合作者那里获得分析的回顾性数据， 旧金山弗朗西斯科（UCSF）。本项目的成功将为动态心脏核成像技术的应用带来新的机遇 通过证明其定量能力在诊断上比目前更有用， 实践的静态成像，因此，将增加更多的价值，CAD的诊断，节省生命和成本。此外 开发新的图像处理方法。我们将致力于自动化动态数据分析， 目前，人工干预的需要是主观性和成本的重要来源。项目组将 包括经验丰富的医学物理学研究人员和动态SPECT成像先驱作为共同研究者， 医学成像数据分析师作为付费顾问，以及核医学医生和核心脏病专家 作为辅助人员。该项目将包括一个重要的学生培训部分， 加州大学旧金山分校如果成功，我们的方法将应用于更大规模的研究，在UCSF进行临床验证， 用于心肌灌注成像的其它成像剂。我们的工作成果将鼓励患者， 医生利用动态心脏核成像，这将增加更多的价值，诊断 CAD，拯救生命，降低成本。

NIH Project Summary (30 lines max) The proposed program aims to improve the diagnosis and prognosis of coronary artery disease (CAD) at clinics by making dynamic nuclear imaging accessible to a wide population. This protocol, currently available primarily at select research facilities, has long been known to be superior for quantitative cardiac perfusion imaging. Yet, dynamic nuclear imaging has almost no penetration in clinical diagnostics. The reason is a combination of several technical challenges, the most noticeable one being its unreasonably long scanning time. Our principal hypothesis is that the unique diagnostic parameters like myocardial blood flow and coronary flow reserve can be obtained by dynamic imaging acquisition over a much shorter scan time than what is being practiced today. Reducing the scan time will remove the primary impediment of translating dynamic nuclear imaging to the clinic. Our objective will be achieved with innovative algorithm development for the two nuclear imaging modalities: single photon emission computed tomography (SPECT) and positron emission tomography (PET). Previously we have developed cutting-edge algorithms based on a novel mathematical tool called non-negative matrix factorization (NMF) for both of these modalities. In this project, we will advance these algorithms significantly toward our objective, while also improving the accuracy of estimating diagnostically valuable information. For this purpose, we will develop additional high-level mathematical components like topology-based analysis of 1D time-series data and anatomical shapes of organs to guide the solution in the data analysis. We will validate our results with realistic numerical phantoms and clinical data with the radiotracer 13NH3 in PET and 99Tc-tetrofosmin in SPECT. Anonymized retrospective data will be available from our collaborators at the University of California, San Francisco (UCSF). The success of our project will bring the application of dynamic cardiac nuclear imaging closer to clinical utilization by proving its quantitative capability to be diagnostically more useful than the currently practiced static imaging, and thus, will add more value to the diagnosis of CAD, saving lives and cost. In addition to developing the new image processing approach. We will work on automating the dynamic data analysis, as currently, the need for manual intervention is a significant source of subjectivity and cost. The project team will include experienced medical physics researchers and dynamic SPECT imaging pioneers as co-investigators, a medical imaging data analyst as a paid consultant, and a nuclear medicine physician and a nuclear cardiologist as supporting personnel. The project will include a vital student training component with collaboration from the UCSF. If successful, our approach will be applied in larger scale studies for clinical validation at UCSF, using additional imaging agents for myocardial perfusion imaging. The result of our work will encourage patients and physicians to take advantage of dynamic cardiac nuclear imaging, which will add more value to the diagnosis of CAD, saving lives, and reducing cost.