title: Robust joint entropy regularization of limited view transmission tomography using Gaussian approximations to the joint histogram.
abstract: Information theoretic measures to incorporate anatomical priors have been explored in the field of emission tomography, but not in transmission tomography. In this work, we apply the joint entropy prior to the case of limited angle transmission tomography. Due to the data insufficiency problem, the joint entropy prior is found to be very sensitive to local optima. Two methods for robust joint entropy minimization are proposed. The first approximates the joint probability density function by a single 2D Gaussian, and is found to be appropriate for reconstructions where the ground truth joint histogram is dominated by two clusters, or multiple clusters that are roughly aligned. The second method is an extension to the case of multiple Gaussians. The intended application for the single Gaussian approximation is digital breast tomosynthesis, where reconstructed volumes are approximately bimodal, consisting mainly of fatty and fibroglandular tissues.
authors.affiliation: University of Oxford, Oxford, United Kingdom. dominique@robots.ox.ac.uk
authors.forename: Dominique
authors.initials: D
authors.lastname: Van de Sompel
authors.forename: Sir Michael
authors.initials: SM
authors.lastname: Brady
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*