Using the stochastic collocation method for the uncertainty quantification of drug concentration due to depot shape variability.
Numerical simulations entail modeling assumptions that impact outcomes. Therefore, characterizing, in a probabilistic sense, the relationship between the variability of model selection and the variability of outcomes is important. Under certain assumptions, the stochastic collocation method offers a computationally feasible alternative to traditional Monte Carlo approaches for assessing the impact of model and parameter variability. We propose a framework that combines component shape parameterization with the stochastic collocation method to study the effect of drug depot shape variability on the outcome of drug diffusion simulations in a porcine model. We use realistic geometries segmented from MR images and employ level-set techniques to create two alternative univariate shape parameterizations. We demonstrate that once the underlying stochastic process is characterized, quantification of the introduced variability is quite straightforward and provides an important step in the validation and verification process.
Preston
J Samuel
JS
Scientific Computing and Imaging Institute and the School of Computing, University of Utah, Salt Lake City, UT 84112, USA. jsam@sci.utah.edu
Tasdizen
Tolga
T
Terry
Christi M
CM
Cheung
Alfred K
AK
Kirby
Robert M
RM
D000465:Algorithms; D000714:Anastomosis, Surgical; D000818:Animals; D000903:Antibiotics, Antineoplastic / Q000493:pharmacokinetics*; D003198:Computer Simulation; D020342:Finite Element Analysis; D006965:Hyperplasia / Q000517:prevention & control; D023421:Models, Animal; D008954:Models, Biological; D015233:Models, Statistical; D009010:Monte Carlo Method; D011138:Polytetrafluoroethylene / Q000737:chemistry; D019736:Prostheses and Implants; D020123:Sirolimus / Q000493:pharmacokinetics*; D013552:Swine
D000903:Antibiotics, Antineoplastic; D011138:Polytetrafluoroethylene; D020123:Sirolimus