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

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.