A method for tomographic three-dimensional 3D reconstruction of a sample including at least one object of interest, the method comprising the steps of: a) obtaining a current set of projection images from an optical tomography system (8, 28); b) adjusting the current set of projection images of the sample according to a priori knowledge to produce adjusted projection images by removing unallowable features and conforming the current set of projection images to allowable features according to the a priori knowledge wherein the a priori knowledge includes a priori knowledge selected from the group consisting of cell preparation chemistry, cell features, cell boundaries, contrast agents having known distributions in contrast, a measured modulation transfer function of the projection system (8,28), Bayesian analysis of multiple similar objects in the sample, and errors flowing from a 3D image reconstruction algorithm; c) using a reconstruction algorithm on the adjusted projection images to generate a 3D image; d) further adjusting the 3D image according to the a priori knowledge to generate an adjusted 3D image; e) applying criteria for process completion to determine whether the adjusted 3D image meets a selected level of confidence values; and f) if the adjusted 3D image does not meet the selected level of confidence values, then computationally creating a set of pseudo projections, where the set of pseudo projections is created computationally by generating artificial projections through the 3D data volume at the same projection angles as the current set of projection images and comparing the current set of projection images with the pseudo projection images to produce a set of new projections, wherein the new projections are input again at step a) as a current set of projection images and steps a) through e) are repeated until the selected level of confidence values is met. a) obtaining a current set of projection images from an optical tomography system (8, 28); b) adjusting the current set of projection images of the sample according to a priori knowledge to produce adjusted projection images by removing unallowable features and conforming the current set of projection images to allowable features according to the a priori knowledge wherein the a priori knowledge includes a priori knowledge selected from the group consisting of cell preparation chemistry, cell features, cell boundaries, contrast agents having known distributions in contrast, a measured modulation transfer function of the projection system (8,28), Bayesian analysis of multiple similar objects in the sample, and errors flowing from a 3D image reconstruction algorithm; c) using a reconstruction algorithm on the adjusted projection images to generate a 3D image; d) further adjusting the 3D image according to the a priori knowledge to generate an adjusted 3D image; e) applying criteria for process completion to determine whether the adjusted 3D image meets a selected level of confidence values; and f) if the adjusted 3D image does not meet the selected level of confidence values, then computationally creating a set of pseudo projections, where the set of pseudo projections is created computationally by generating artificial projections through the 3D data volume at the same projection angles as the current set of projection images and comparing the current set of projection images with the pseudo projection images to produce a set of new projections, wherein the new projections are input again at step a) as a current set of projection images and steps a) through e) are repeated until the selected level of confidence values is met.