title: Ordered subsets convex algorithm for 3D terahertz transmission tomography.
abstract: We investigate in this paper a new reconstruction method in order to perform 3D Terahertz (THz) tomography using a continuous wave acquisition setup in transmission mode. This method is based on the Maximum Likelihood for TRansmission tomography (ML-TR) first developed for X-ray imaging. We optimize the Ordered Subsets Convex (OSC) implementation of the ML-TR by including the Gaussian propagation model of THz waves and take into account the intensity distributions of both blank calibration scan and dark-field measured on THz detectors. THz ML-TR reconstruction quality and accuracy are discussed and compared to other tomographic reconstructions.
authors.forename: B
authors.initials: B
authors.lastname: Recur
authors.forename: H
authors.initials: H
authors.lastname: Balacey
authors.forename: J
authors.initials: J
authors.lastname: Bou Sleiman
authors.forename: J B
authors.initials: JB
authors.lastname: Perraud
authors.forename: J-P
authors.initials: JP
authors.lastname: Guillet
authors.forename: A
authors.initials: A
authors.lastname: Kingston
authors.forename: P
authors.initials: P
authors.lastname: Mounaix
mesh_terms: D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted; D019047:Phantoms, Imaging; D011336:Probability; D014056:Tomography, X-Ray / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000379:methods*