Artefacts are common in today's cone beam CT (CBCT). They are induced by discrepancies between the mathematical modelling and the actual physical imaging process. Since artefacts may interfere with the diagnostic process performed on CBCT data sets, every user should be aware of their presence. This article aims to discuss the most prominent artefacts identified in the scientific literature and review the existing knowledge on these artefacts. We also briefly review the basic three-dimensional (3D) reconstruction concept applied by today's CBCT scanners, as all artefacts are more or less directly related to it.

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Artefacts in CBCT: a review

A new iterative maximum-likelihood reconstruction algorithm for X-ray computed tomography is presented. The algorithm prevents beam hardening artifacts by incorporating a polychromatic acquisition model. The continuous spectrum of the X-ray tube is modeled as a number of discrete energies. The energy dependence of the attenuation is taken into account by decomposing the linear attenuation coefficient into a photoelectric component and a Compton scatter component. The relative weight of these components is constrained based on prior material assumptions. Excellent results are obtained for simulations and for phantom measurements. Beam-hardening artifacts are effectively eliminated. The relation with existing algorithms is discussed. The results confirm that improving the acquisition model assumed by the reconstruction algorithm results in reduced artifacts. Preliminary results indicate that metal artifact reduction is a very promising application for this new algorithm.

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An iterative maximum-likelihood polychromatic algorithm for CT

Transform methods for image reconstruction from projections are based on analytic inversion formulas. In this tutorial paper, the inversion formula for the case of two-dimensional (2-D) reconstruction from line integrals is manipulated into a number of different forms, each of which may be discretized to obtain different algorithms for reconstruction from sampled data. For the convolution-backprojection algorithm and the direct Fourier algorithm the emphasis is placed on understanding the relationship between the discrete operations specified by the algorithm and the functional operations expressed by the inversion formula. The performance of the Fourier algorithm may be improved, with negligible extra computation, by interleaving two polar sampling grids in Fourier space. The convolution-backprojection formulas are adapted for the fan-beam geometry, and other reconstruction methods are summarized, including the rho-filtered layergram method, and methods involving expansions in angular harmonics. A standard mathematical process leads to a known formula for iterative reconstruction from projections at a finite number of angles. A new iterative reconstruction algorithm is obtained from this formula by introducing one-dimensional (1-D) and 2-D interpolating functions, applied to sampled projections and images, respectively. These interpolating functions are derived by the same Fourier approach which aids in the development and understanding of the more conventional transform methods.

Reconstruction algorithms: Transform methods

The introduction in 1998 of multi-detector row computed tomography (CT) by the major CT vendors was a milestone with regard to increased scan speed, improved z-axis spatial resolution, and better utilization of the available x-ray power. In this review, the general technical principles of multi-detector row CT are reviewed as they apply to the established four- and eight-section systems, the most recent 16-section scanners, and future generations of multi-detector row CT systems. Clinical examples are used to demonstrate both the potential and the limitations of the different scanner types. When necessary, standard single-section CT is referred to as a common basis and starting point for further developments. Another focus is the increasingly important topic of patient radiation exposure, successful dose management, and strategies for dose reduction. Finally, the evolutionary steps from traditional single-section spiral image-reconstruction algorithms to the most recent approaches toward multisection spiral reconstruction are traced.

Multi-detector row CT systems and image-reconstruction techniques.

It is often desired to calculate line integrals through a field of reconstructed CT density pixels for the purpose of improving CT image quality. Two algorithms widely published and discussed in the past are known to either degrade spatial resolution or generate errors in the results due to the discontinuous "square pixel" modeling of the reconstructed image. An algorithm is described, based on linear interpolation between pixels, which provides superior accuracy without unnecessary loss of resolution. It was tested on simulated data for a head section and on a narrow Gaussian density distribution. The experimental results demonstrated improved performance. The method is expected to prove useful for many types of post-reconstruction processing, including beam hardening, missing data, and noise supression algorithms.

An Improved Algorithm for Reprojecting Rays through Pixel Images

The exponential edge-gradient effect must arise in any x-ray transmission CT scanner whenever long sharp edges of high contrast are encountered. The effect is non-linear and is due to the interaction of the exponential law of x-ray attenuation and the finite width of the scanning beam in the x-y plane. The error induced in the projection values is proved to be always negative. While the most common effect is lucent streaks emerging from single straight edges, it is demonstrated that dense streaks from pairs of edges are possible. It is shown that an exact correction of the error is possible only under very special (and rather unrealistic) circumstances in which an infinite number of samples per beam width are available and all thin rays making up the beam can be considered parallel. As a practical matter, nevertheless, increased sample density is highly desirable in making good approximate corrections; this is demonstrated with simulated scans. Two classes of approximate correction algorithms are described and their effectiveness evaluated on simulated CT phantom scans. One such algorithm is also shown to work well with a real scan of a physical phantom on a machine that provides approximately four samples per beam width.

http://iopscience.iop.org/article/10.1088/0031-9155/26/3/010/pdf

R D Spital

Papers

The exponential edge-gradient effect in x-ray computed tomography.