Kolehmainen Ville, Schweiger Martin, Nissilä Ilkka, Tarvainen Tanja, Arridge Simon R, Kaipio Jari P
Department of Physics, University of Kuopio, P.O. Box 1627, 70211 Kuopio, Finland.
J Opt Soc Am A Opt Image Sci Vis. 2009 Oct;26(10):2257-68. doi: 10.1364/JOSAA.26.002257.
Model reduction is often required in diffuse optical tomography (DOT), typically because of limited available computation time or computer memory. In practice, this means that one is bound to use coarse mesh and truncated computation domain in the model for the forward problem. We apply the (Bayesian) approximation error model for the compensation of modeling errors caused by domain truncation and a coarse computation mesh in DOT. The approach is tested with a three-dimensional example using experimental data. The results show that when the approximation error model is employed, it is possible to use mesh densities and computation domains that would be unacceptable with a conventional measurement error model.
