Proceedings in applied mathematics & mechanics, Dec 1, 2021
Biomechanical modeling enables a better understanding and prediction of various processes in the ... more Biomechanical modeling enables a better understanding and prediction of various processes in the human body. To make this simulation more patient‐specific, realistic geometries of liver lobules are included in an existing knowledge‐based model for the simulation of hepatic function‐perfusion processes in the human liver. This model allows the simulation of liver diseases such as the non‐alcoholic fatty liver disease (NAFLD) or tumor development. The basis of this calculation is a continuum‐biomechanical multiscale and multiphase model based on the Theory of Porous Media. To capture the function‐perfusion and growth processes in the liver, partial differential equations (PDEs) on the lobule scale are coupled to ordinary differential equations (ODEs) describing metabolic processes on the cellular scale. Additionally, we used manifold learning techniques on in silico data for the identification of inverted fat zonation during NAFLD.
Proceedings in applied mathematics & mechanics, Dec 1, 2021
Biomechanical modeling enables a better understanding and prediction of various processes in the ... more Biomechanical modeling enables a better understanding and prediction of various processes in the human body. To make this simulation more patient‐specific, realistic geometries of liver lobules are included in an existing knowledge‐based model for the simulation of hepatic function‐perfusion processes in the human liver. This model allows the simulation of liver diseases such as the non‐alcoholic fatty liver disease (NAFLD) or tumor development. The basis of this calculation is a continuum‐biomechanical multiscale and multiphase model based on the Theory of Porous Media. To capture the function‐perfusion and growth processes in the liver, partial differential equations (PDEs) on the lobule scale are coupled to ordinary differential equations (ODEs) describing metabolic processes on the cellular scale. Additionally, we used manifold learning techniques on in silico data for the identification of inverted fat zonation during NAFLD.
Uploads
Papers by Tim Ricken