en Research Paper Research Paper We present an efficient method for the reduction of model equations in the linearized diffuse optical tomography (DOT) problem. We first implement the maximum a posteriori (MAP) estimator and Tikhonov regularization, which are based on applying preconditioners to linear perturbation equations. For model reduction, the precondition is split into two parts: the principal components are considered as reduced size preconditioners applied to linear perturbation equations while the less important components are marginalized as noise. Simulation results illustrate that the new proposed method improves the image reconstruction performance and localizes the abnormal section well with a better computational efficiency. DOT,Bayesian Methods,Model Reduction,Preconditioner, 35 45 http://ijeee.iust.ac.ir/browse.php?a_code=A-10-3-33&slc_lang=en&sid=1 A.-R. Zirak زیرک 18003194753284600127 18003194753284600127 Yes M. Khademi خادمی 18003194753284600128 18003194753284600128 No M.-S. Mahloji محلوجی 18003194753284600129 18003194753284600129 No