MISSING DATA ESTIMATION FOR FULLY 3D SPIRAL CT IMAGE RECONSTRUCTION

Abstract

Reconstruction algorithms that are not set up to handle in-complete datasets can lead to artifacts in the reconstructed images because the assumptions regarding the size of the image space and/or data space are violated. In this study, two recently developed geometry-independent methods 1 are applied to fully 3D multi-slice spiral CT image recon-struction. Using simulated and clinical datasets, we dem-onstrate the effectiveness of the missing data approaches in improving the quality of slices that have experienced truncation in either the transverse or longitudinal direction. • When the support of an object lies partially outside the field of view (FOV) of a CT scanner, artifacts may arise in the reconstructed image due to undersampling. • Most reconstruction algorithms implicitly assume the entire object is confined to the FOV, but if this is not the case, excessively large attenuation values may be recon-structed inside the boundary of the FOV. • The reconstruction algorithm is unaware that the mea-sured data has been affected by the object's attenuation outside the FOV, so the image in the FOV is recon-structed such that the projections through it match the measured data