Diffusion optical tomography using entropic priors
ABSTRACT Diffuse optical tomography (DOT) is a functional imaging modality which aims to retrieve the optical characteristics of the probed tissue, namely light absorption and diffusion. The accurate retrieval of the spatial distribution for each optical characteristic involves the solution of a highly-ill posed, non-linear inverse problem, thus employing a regularization is essential. In this work, we propose an entropic regularization scheme for DOT reconstruction that uses a priori structural information through mutual information (MI) and joint entropy (JE).We compare MI and JE through simulations that illustrate their behavior when the reference and DOT images are not identical in structure. We propose an efficient implementation of these regularizers based on fast Fourier transforms. The method is tested through numerical simulations.
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ABSTRACT: Diffuse optical tomography (DOT) is an ongoing medical imaging modality in which tissue is illuminated by near-infrared light from an array of sources, the multiply-scattered light which emerges is observed with an array of detectors, and then a model of the propagation physics is used to infer the localized optical properties of the illuminated tissue. The three primary absorbers at these wavelengths, water and both oxygenated and deoxygenated hemoglobin, all have relatively weak absorption. This fortuitous fact provides a spectral window through which we can attempt to localize absorption (primarily by the two forms of hemoglobin) and scattering in the tissue. The most important current applications of DOT are detecting tumors in the breast and imaging the brain. We introduce the basic idea of DOT and review the history of optical methods in medicine as relevant to the development of DOT. We then detail the concept of DOT, including a review of the tissue's optical properties, modes of operation for DOT, and the challenges which the development of DOT must overcome. The basics of modelling the DOT forward problem and some critical issues among the numerous implementations that have been investigated for the DOT inverse problem, with an emphasis on signal processing. We summarize with some specific results as examples of the current state of DOT researchIEEE Signal Processing Magazine 12/2001; · 3.37 Impact Factor
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ABSTRACT: We present a review of methods for the forward and inverse problems in optical tomography. We limit ourselves to the highly scattering case found in applications in medical imaging, and to the problem of absorption and scattering reconstruction. We discuss the derivation of the diffusion approximation and other simplifications of the full transport problem. We develop sensitivity relations in both the continuous and discrete case with special concentration on the use of the finite element method. A classification of algorithms is presented, and some suggestions for open problems to be addressed in future research are made.Inverse Problems 12/1998; 15(2):R41. · 1.90 Impact Factor
Conference Proceeding: The use of mutual information and joint entropy for anatomical priors in emission tomography[show abstract] [hide abstract]
ABSTRACT: This paper studies the use of mutual information and joint entropy to define anatomical priors for maximum-a- posteriori (MAP) reconstruction in emission tomography. Other groups have used mutual information for this purpose, and reported promising results. Simple simulation studies with the "isolated" prior distribution reveal that mutual information may introduce bias, because of a repelling effect between intensity clusters in the marginal histogram. Deleting the terms involving the marginal histograms leads to the joint entropy prior. A gradient ascent MAP-reconstruction algorithm with this prior is described. Its performance is studied with simulation experiments and illustrated on a two sets of patient data: a whole body PET/CT scan and a PET brain scan, combined with the corresponding MRI-scan using off-line registration. The joint entropy seems to be a useful function for defining anatomical priors that do not require explicit segmentation of the anatomical image.Nuclear Science Symposium Conference Record, 2007. NSS '07. IEEE;