Spatial deconvolution technique to improve the accuracy of reconstructed three-dimensional diffuse optical tomographic images

Abstract

A straightforward spatial deconvolution operation is presented that seeks to invert the information-blurring property of first-order perturbation algorithms for diffuse optical tomography (DOT) image reconstruction. The method that was developed to generate these deconvolving operators, or filters, was conceptually based on the frequency-encoding process used in magnetic resonance imaging. The computation of an image-correcting filter involves the solution of a large system of linear equations, in which known true distributions and the corresponding recovered distributions are compared. Conversely, application of a filter involves only a simple matrix multiplication. Simulation results show that application of this deconvolution operation to three-dimensional DOT images reconstructed by the solution of a first-order perturbation equation (Born approximation) can yield marked enhancement of image quality. In the examples considered, use of image-correcting filters produces obvious improvements in image quality, in terms of both location and mirco(a) of the inclusions. The displacements between the true and recovered locations of an inclusion's centroid location are as small as 1 mm, in an 8-cm-diameter medium with 1.5-cm-diameter inclusions, and the peak value of the recovered micro(a) for the inclusions deviates from the true value by as little as 5%.