title: Joint reconstruction of multi-channel, spectral CT data via constrained total nuclear variation minimization.
abstract: We explore the use of the recently proposed 'total nuclear variation' (TVN) as a regularizer for reconstructing multi-channel, spectral CT images. This convex penalty is a natural extension of the total variation (TV) to vector-valued images and has the advantage of encouraging common edge locations and a shared gradient direction among image channels. We show how it can be incorporated into a general, data-constrained reconstruction framework and derive update equations based on the first-order, primal-dual algorithm of Chambolle and Pock. Early simulation studies based on the numerical XCAT phantom indicate that the inter-channel coupling introduced by the TVN leads to better preservation of image features at high levels of regularization, compared to independent, channel-by-channel TV reconstructions.
authors: Department of Radiology, The University of Chicago, Chicago, Illinois 60637, USA.
authors: David S
authors: DS
authors: Rigie
authors: Patrick J
authors: PJ
authors: La Rivière
mesh_terms: D000465:Algorithms; D001842:Bone and Bones / Q000000981:diagnostic imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D019047:Phantoms, Imaging; D017785:Photons; D011829:Radiation Dosage; D011857:Radiographic Image Interpretation, Computer-Assisted; D014057:Tomography, X-Ray Computed / Q000379:methods*