Iterative reconstruction of incomplete tomography data: application cases

The method of Computed Tomography (CT) has progressed throughout the past decade with advances in CT apparatus and program parts that have resulted in an increasing number of CT applications. Today innovative CT Xray detectors have high spatial resolution till a tenth or hundredth of a micron. However, itsfield of view is significantly limited. The object being scanned with a high resolution does not always completely enter in (covered by) the field of view of the detector. The collected projections data may be incomplete. The use of incomplete data in classical reconstruction methods leads to image quality loss. This paper provides a new advanced reconstruction method that demonstrates image quality improvements compared with classical methods when incomplete data collected. The method uses the hypothesis about the consistency of object description in sinogram space and reconstruction space. Input data for the algorithm proposed are incomplete data, and the output data are the reconstructed image and the confidence values for all pixels of the image (reconstruction reliability). A detailed description of the algorithm is presented. Its quality characteristics are based on Shepp-Logan phantom studies.