Sparse-View X-Ray CT Reconstruction using CAD Model Registration

Abstract

X-ray Computed Tomography is a powerful non-destructive testing tool increasingly used by manufacturers to ensure the conformity of the produced parts. Despite growing interest, it is struggling to establish itself in online testing applications due to the large number of X-ray projections required to ensure a good reconstructed image. To reduce this number of projections from a few hundred to a few dozens while still getting satisfying reconstruction quality, we propose to infer a so-called mask on the volume to be reconstructed. By constraining the back-projection of the acquired X-ray projections only on this mask, corresponding to the voxels of the volume containing matter, iterative reconstruction algorithms, already very efficient at a low number of views compared to the traditional FDK, can better reconstruct an object, and with fewer computational resources. However, this technique requires a preliminary step: registering the experimental data to the a priori mask data. This paper presents a 3D/2D registration method based on Iterative Inverse Perspective Matching that registers a 3D CAD model to experimental projections. Then, we will explain how to construct the mask and use it during the reconstruction.