title: A denoising algorithm for projection measurements in cone-beam computed tomography.
abstract: The ability to reduce the radiation dose in computed tomography (CT) is limited by the excessive quantum noise present in the projection measurements. Sinogram denoising is, therefore, an essential step towards reconstructing high-quality images, especially in low-dose CT. Effective denoising requires accurate modeling of the photon statistics and of the prior knowledge about the characteristics of the projection measurements. This paper proposes an algorithm for denoising low-dose sinograms in cone-beam CT. The proposed algorithm is based on minimizing a cost function that includes a measurement consistency term and two regularizations in terms of the gradient and the Hessian of the sinogram. This choice of the regularization is motivated by the nature of CT projections. We use a split Bregman algorithm to minimize the proposed cost function. We apply the algorithm on simulated and real cone-beam projections and compare the results with another algorithm based on bilateral filtering. Our experiments with simulated and real data demonstrate the effectiveness of the proposed algorithm. Denoising of the projections with the proposed algorithm leads to a significant reduction of the noise in the reconstructed images without oversmoothing the edges or introducing artifacts.
authors.affiliation: Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada. Electronic address: karimi@ece.ubc.ca.
authors.forename: Davood
authors.initials: D
authors.lastname: Karimi
authors.affiliation: Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada.
authors.forename: Rabab
authors.initials: R
authors.lastname: Ward
mesh_terms: D000465:Algorithms; D054893:Cone-Beam Computed Tomography / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D059629:Signal-To-Noise Ratio
keywords: Bregman method; Cone-beam; Low-dose computed tomography; Sinogram denoising; Sparsity-based denoising; Total variation denoising