Reconstruction Approach for Partially Truncated CT Data

Abstract: For various reasons it might be required to scan an object that partially lies outside the field of view(FOV) of a CT scanner. The parts of the object that lie outside the FOV will not contribute to the line integrals measured by the detector which will cause image artifacts that affect the final image quality. In this paper, I suggest a novel reconstruction approach that estimates the attenuation by the object outside the FOV using a priori knowledge about the outline of the object. It is shown that, knowing the object’s outline, it is possible to determine whether the attenuation along a given line is truncated. The total attenuation for a truncated projection is then estimated by interpolating the data between the consistent projections. The method therefore requires some of the projections to be consistent. This estimate, along with the knowledge of the distance traversed by the X-Ray inside the object is then used to determine the average attenuation. The method was tested on both numerical and physical phantoms. The results are satisfactory even when up to 80% of the projections are truncated. Structural Similarity Index (SSIM) was compared for the complete reconstructed images,and regions of truncations before and after the algorithm was applied. Reconstructed images from completely consistent projections served as ground truth. The results indicate that the algorithm can be used to reconstruct partially truncated CT data, which was tested on numerical and physical phantoms (of semicircular cross section). There is scope for further testing of the algorithm on irregularly shaped objects.