An auto-focus method for generating sharp 3D tomographic images

Abstract

We present a simple, robust, and versatile solution to the problem of blurred tomographic images as a result of imperfect geometric hardware alignment. The necessary precision for the alignment between the various components of a tomographic instrument is in many cases technologically difficult to implement, or requires impractical stability. Misaligned projection sets are not self-consistent and give blurred tomographic reconstructions. We have developed an off-line software method that utilises a geometric model to parameterise the alignment, and an algorithm for determining the alignment parameter set that gives the sharpest tomogram. It is an adaptation of passive auto-focus methods that have been used to obtain sharp images in optical instruments for decades. To minimise computation time, the auto-focus strategy is a multi-scale iterative technique implemented on a selection of 2D cross-sections of the tomogram. For each cross-section, the sharpness is evaluated while scanning over various combinations of alignment parameters. The parameter set that maximises sharpness is used to reconstruct the 3D tomogram. To apply the corrections, the projection data are re-mapped, or the reconstruction algorithm is modified. The entire alignment process takes less time than that of a full-scale 3D reconstruction. It can in principle be applied to any cone or parallel beam CT with circular, helical, or more general trajectories. It can also be applied retrospectively to archived projection data without any additional information. This concept is fully tested and implemented for routine use in the ANU micro-CT reconstruction software suite and has made the entire reconstruction pipeline robust and autonomous. Show more