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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.
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https://hal.archives-ouvertes.fr/hal-01066999
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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, Optical Society of America, 2014, 22 (19), pp.23299-23309. ⟨10.1364/OE.22.023299⟩. ⟨hal-01066999⟩

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