Ordered subsets convex algorithm for 3D terahertz transmission tomography

Benoît Recur (1) , Hugo Balacey (2, 3) , Joyce Bou Sleiman (2) , Jean-Baptiste Perraud (2) , Jean-Paul Guillet (4) , Andrew Kingston (1) , Patrick Mounaix (2)

1 Department of Applied Mathematics (Canberra)
2 LOMA - Laboratoire Ondes et Matière d'Aquitaine
3 Noctylio SAS
4 IMS - Laboratoire de l'intégration, du matériau au système

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.

Mots clés: (100.6890) Three-dimensional image processing; (170.3010) Image reconstruction techniques; (120.5800) Scanners.; (120.5800) Scanners; (110.6955) Tomographic imaging; (110.6795) Terahertz imaging

Domaines: Optique [physics.optics]

Benoît Recur, Hugo Balacey, Joyce Bou Sleiman, Jean-Baptiste Perraud, Jean-Paul Guillet, et al.. Ordered subsets convex algorithm for 3D terahertz transmission tomography. Optics Express, 2014, 22 (19), pp.23299-23309. ⟨10.1364/OE.22.023299⟩. ⟨hal-01066999⟩