CRII: ACI: Transforming Semiautomatic Patient-Specific Simulation Workflows into Autonomous Medical Imaging-Through-Analysis Tools

虽然有限元模拟已经在医学研究中得到了很好的确立，但它们在临床实践中日常使用的潜力仍然很大程度上是闲置的。其中一个主要原因是建立特定于患者的计算模型的过程，包括将诊断成像数据传输到显式表面、几何清理和边界适配网格生成。尽管目前已经有了强大的软件解决方案来简化这一过程，但许多涉及复杂生理几何形状的模拟工作流程仍然需要经过专门培训的分析师的干预。相关的成本和时间影响不适合许多以预算紧张和紧急决策为特征的临床过程。该项目的目标是启动研究活动，为预测模拟在临床决策中的更紧密集成提供一条途径，并帮助释放其在临床例行公事中的潜力。开发的概念和方法可以应用于对骨质疏松症和血管疾病的及时诊断和管理，这些疾病构成了美国医疗体系的重大挑战。这项研究的发现和临床工具有可能改变目前的医疗保健方案，从而改善美国很大一部分老年人口的福祉。因此，这项研究与NSF促进科学进步、促进国家健康、繁荣和福祉的使命是一致的。从技术角度来看，该项目设想了无缝成像通过分析程序，使预测性生物医学模拟从诊断成像数据的读取到临床相关模拟结果的输出完全自动化。为此，它着重于建立基于扩散几何的有限元分析的数学、算法和技术基础，应用于骨力学和生物流体的临床相关问题。中心点是相场概念在扩散意义上的扩展，以描述所有与界面有关的信息，如位置或法向。在模糊成像数据的背景下，通过将无监督医学图像处理技术与变分分割方法相结合，可以自动生成漫射几何模型。漫反射几何可以使专门的有限元方法将所有变分边界和界面积分转换为体积积分，从而避免任何形式的显式曲面跟踪。该项目旨在展示基于漫反射几何的模拟的准确性、稳健性和误差控制，特别是在生理相关的流型范围内。它还可以展示弥散几何学方法在自主网络基础设施中用于骨质疏松症预测、骨科手术前规划和肝脏血流灌注分析的潜力。

While finite element simulations are well established in medical research, their potential for every-day use in clinical practice still lies largely idle. One of the major reasons is the process of building patient-specific computational models, including transfer of diagnostic imaging data to explicit surfaces, geometry cleanup and boundary-fitted mesh generation. Although powerful software solutions to streamline this process are available today, many simulation workflows involving complex physiological geometries still require the intervention of specially trained analysts. The associated cost and time implications do not fit into many clinical processes characterized by tight budgets and urgent decision-making. The goal of this project is to initiate research activities that provide a pathway to a closer integration of predictive simulation in clinical decision-making and help unlock its potential in clinical routines. The developed concepts and methods can be applied in the context of the timely diagnosis and management of osteoporosis and vascular disease that constitute significant challenges for the US healthcare system. The discoveries and clinical tools evolving from this research have the potential to transform current healthcare protocols, thus improving the well-being of a large portion of the elderly population in the US. Therefore, this research aligns with the NSF mission to promote the progress of science and to advance the national health, prosperity and welfare.From a technical viewpoint, this project envisions seamless imaging-through-analysis procedures that enable the full automation of predictive biomedical simulations from reading in diagnostic imaging data to the output of clinically relevant simulation results. To this end, it focuses on establishing the mathematical, algorithmic and technical foundations of diffuse geometry based finite element analysis applied to clinically relevant problems in bone mechanics and biofluids. The central point is the extension of the phase-field concept to the description of all interface related information, such as location or normal directions, in a diffuse sense. In the context of fuzzy imaging data, diffuse geometric models can be generated automatically by integrating unsupervised medical image processing techniques with variational segmentation methods. Diffuse geometry can enable specialized finite element methods that transfer all variational boundary and interface integrals into volume integrals, so that any form of explicit surface tracking is avoided. This project aims to demonstrate accuracy, robustness, and error control in diffuse geometry based simulations, in particular within the range of physiologically relevant flow regimes. It also can demonstrate the potential of diffuse geometry methods in autonomous cyberinfrastructure for osteoporosis prediction, preoperative planning for orthopedics, and liver perfusion analysis.