Iterative CT Reconstruction on Limited Angle Trajectories Applied to Robotic Inspection

Robotic inspection is one of the acknowledged new trends in X-ray Non Destructive testing (NDT) since it allows more flexibility in the acquisition trajectory and therefore a valued adaptability to object and environment constraints. In this context, we are developing an advanced Computed Tomography (CT) robotic platform consisting of two robots equipped, with a micro-focus X-ray tube and a flat panel detector, respectively. In parallel to the equipment installation, we propose to address the new challenges brought by this robotic inspection. In particular we focus on 3D iterative reconstruction algorithms that deal with few and limited-angle data. For this purpose, we consider regularized algebraic methods. In particular, we propose two algorithms named SART-FISTA-TV and DART-FISTA-TV. The first one is based on the common used SART [1] algorithm and the second is based on Discrete Algebraic Reconstruction Technique (DART) [2] which is an algebraic algorithm that incorporates prior knowledge about the different materials (attenuation coefficient) of the scanned object into the reconstruction process. The two proposed algorithms use Total Variation (TV) regularization and Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) to increase the convergence speed. For performance evaluation, we illustrate a numerical comparison of these algorithms from both complete (noiseless and noisy) and truncated data generated on a reverse helix of angular range limited to 150 degrees. CIVA [3] software is used to simulate these projection data. We also show reconstruction results using the robotic inspection platform with a view angle limited to 150 degrees and a reduced number of projections.