Purpose: The authors present a robust algorithm that removes the blurring and double-edge artifacts in high-resolution computed tomography (CT) images that are caused by misaligned scanner components. This alleviates the time-consuming process of physically aligning hardware, which is of particular benefit if components are moved or swapped frequently.
Methods: The proposed method uses the experimental data itself for calibration. A parameterized model of the scanner geometry is constructed and the parameters are varied until the sharpest 3D reconstruction is found. The concept is similar to passive auto-focus algorithms of digital optical instruments. The parameters are used to remap the projection data from the physical detector to a virtual aligned detector. This is followed by a standard reconstruction algorithm, namely the Feldkamp algorithm. Feldkamp et al. [J. Opt. Soc. Am. A 1, 612-619 (1984)].
Results: An example implementation is given for a rabbit liver specimen that was collected with a circular trajectory. The optimal parameters were determined in less computation time than that for a full reconstruction. The example serves to demonstrate that (a) sharpness is an appropriate measure for projection alignment, (b) our parameterization is sufficient to characterize misalignments for cone-beam CT, and (c) the procedure determines parameter values with sufficient precision to remove the associated artifacts.
Conclusions: The algorithm is fully tested and implemented for regular use at The Australian National University micro-CT facility for both circular and helical trajectories. It can in principle be applied to more general imaging geometries and modalities. It is as robust as manual alignment but more precise since we have quantified the effect of misalignment.