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

虽然有限元模拟在医学研究中得到了很好的应用，但它们在临床实践中的日常应用潜力仍然很大。其中一个主要原因是建立特定患者计算模型的过程，包括将诊断成像数据转移到显式表面，几何清理和边界拟合网格生成。虽然今天有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临床过程的特点是紧张的预算和紧急决策。该项目的目标是启动研究活动，为临床决策中预测模拟的更紧密集成提供途径，并帮助释放其在临床常规中的潜力。开发的概念和方法可以应用于骨质疏松症和血管疾病的及时诊断和管理，这些疾病对美国医疗保健系统构成重大挑战。从这项研究中发展出来的发现和临床工具有可能改变当前的医疗协议，从而改善美国大部分老年人的福祉。因此，这项研究与美国国家科学基金会促进科学进步和促进国家健康、繁荣和福利的使命是一致的。从技术角度来看，该项目设想了从成像到分析的无缝流程，使预测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，它侧重于建立基于漫射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。其中心点是将相场概念扩展到所有界面相关信息的描述，如位置或法线方向，在扩散意义上。在图像数据模糊的情况下，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。漫射几何可以实现专门的有限元方法，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表面跟踪。该项目旨在展示基于漫射几何模拟的准确性、鲁棒性和误差控制，特别是在与生理相关的流动状态范围内。它还可以证明弥散几何方法在骨质疏松症预测、骨科术前规划和肝脏灌注分析的自主网络基础设施中的潜力。

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.