Abstract

In the field of non-destructive testing, Terahertz (THz) tomography is a modern imaging technique permitting 3D inspection of opaque objects. A tomographic procedure reconstructs a 3D volume of the acquired object by intersecting the data contained on the projection set. This depth information is extracted from several projections acquired around the object at different viewing angles. This property has made THz tomography a complementary technique to X-Rays. Several reconstruction methods have been investigated for THz tomography [1] but they must be adapted to THz wave properties. For monochromatic source, Beer-Lambert law describes the attenuation encountered by THz waves through the sample and measured on the detector, when reflexion and refraction effects are neglected. Then, as their complements in X-Ray tomography, we investigate in this paper on a Maximum-Likelihood expectation-maximization for TRansmission tomography (ML-TR) compatible with THz radiations. This method, based on Poisson distribution model of measured radiations, takes into account the Gaussian propagation of THz beam [2] and allows introducing some a priori knowledge about the imaged object. Especially, we focus on the implementation denoted Ordered Subsets Convex (OSC) algorithm since it has an efficient convergence rate despite of the noise level and the sparsity of acquired data [3]. We discuss how this new reconstruction is able to estimate physical properties of samples acquired with a 100GHz/300GHz scanner in addition to the 3D reconstruction. Moreover, we present an innovative data and image processing sequence to perform non-destructive inspection from 3D terahertz (THz) images. After a 3D tomographic reconstruction of a sample, a preset segmentation affords the different regions of interest (ROI) composing the inert part of sample. Then a 3D visualization and dimensional measurements could be performed on these ROI, separately, in order to provide new informations of the studied sample.

© 2014 Optical Society of America