ML parameter estimation for Markov random fields with applications to Bayesian tomography
IEEE Transactions on Image Processing
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This paper addresses fluorescence diffuse optical tomography (FDOT) reconstruction problem with a priori information. We assume that approximate location of the fluorophore concentration is known from physiology of the disease and nature of the fluorophore injected. In addition, we assume the anatomical edge structures from an anatomical image modality partially coincident with the edges of the fluorophore concentration image. We formulate FDOT reconstruction in Bayesian framework and model a priori information of FDOT into a Gaussian Markov random field (GMRF) with several unknown parameters. We simultaneously estimate the optical image and the unknown a priori model parameters. Numerical simulations demonstrate that the a priori information could effectively improve the image reconstruction results.