We show that even without prior knowledge about materials or spectrum, effective beam hardening correction can be obtained.

We present a survey of techniques for the reduction of streaking artefacts caused by metallic objects in X-ray Computed Tomography (CT) images. A comprehensive review of the existing state-of-the-art Metal Artefact Reduction (MAR) techniques, drawn predominantly from the medical CT literature, is supported by an experimental comparison of twelve MAR techniques. The experimentation is grounded in an evaluation based on a standard scientific comparison protocol for MAR methods, using a software generated medical phantom image as well as a clinical CT scan. The experimentation is extended by considering novel applications of CT imagery consisting of metal objects in non-tissue surroundings acquired from the aviation security screening domain. We address the shortage of thorough performance analyses in the existing MAR literature by conducting a qualitative as well as quantitative comparative evaluation of the selected techniques. We find that the difficulty in generating accurate priors to be the predominant factor limiting the effectiveness of the state-of-the-art medical MAR techniques when applied to non-medical CT imagery. This study thus extends previous works by: comparing several state-of-the-art MAR techniques; considering both medical and non-medical applications and performing a thorough performance analysis, considering both image quality as well as computational demands.

Purpose: In iterative reconstruction, metal artifacts can be reduced by applying more accurate reconstruction models that are usually also more computationally demanding. The hypothesis of this work is that these complex models only need to be applied in the vicinity of the metals and that a less complex model can be used for the remainder of the reconstruction volume.Method: A method is described that automatically divides the reconstruction volume into metal and nonmetal regions. The different regions are called patches. A different energy and resolution model can be assigned to each of the patches. The patches containing metals are reconstructed with a fully polychromatic spectral model (IMPACT) and if necessary with an increased resolution model. The patch without metals is reconstructed with a simple polychromatic model (MLTRC) that only includes the spectral behavior of water attenuation. Comparing the computational complexity of IMPACT and MLTRC gives a ratio of 8:3. The different patches are updated sequentially as in a grouped coordinate algorithm. Two phantoms were simulated and measured: a circular phantom containing small metal cylinders and a body phantom representing a human pelvis with two femoral implants. As a first test, the sequential update of the patches was applied while using the same energy model for all patches. Secondly, the local model approach was applied using MLTRC for non-metal regions and IMPACT for metal regions. The results of different iterative reconstruction schemes are compared to the results of projection completion, another important method for the reduction of metal artifacts.Results: Reconstruction schemes including the sequential update of the patches result in images with less streak artifacts compared to a regular reconstruction. The sequential update of each of the metal regions improves the relative convergence of the metals (edges and attenuation values) against the rest of the image, which leads to an improved artifact reduction. Using the combined IMPACT+MLTRC model results in a similar image quality as using IMPACT everywhere, while providing an important benefit regarding computational complexity. Some streak and shadow artifacts were still present, but all structures present in the phantom could be observed. Projection completion results in reconstructions with less obvious streak and shadow artifacts but tends to deform or erase structures lying close to or in between metallic structures. Conclusions:Metal artifact reduction with iterative reconstruction can be achieved by using complex models only locally without losing image quality. Separately updating metal regions leads to reduced streak artifacts. Structures lying close to or in between metals are often better reconstructed, compared to projection completion results, because all available information is used.

Positron emission tomography data are typically reconstructed with maximum likelihood expectation maximization (MLEM). However, MLEM suffers from positive bias due to the non-negativity constraint. This is particularly problematic for tracer kinetic modeling. Two reconstruction methods with bias reduction properties that do not use strict Poisson optimization are presented and compared to each other, to filtered backprojection (FBP), and to MLEM. The first method is an extension of NEGML, where the Poisson distribution is replaced by a Gaussian distribution for low count data points. The transition point between the Gaussian and the Poisson regime is a parameter of the model. The second method is a simplification of ABML. ABML has a lower and upper bound for the reconstructed image whereas AML has the upper bound set to infinity. AML uses a negative lower bound to obtain bias reduction properties. Different choices of the lower bound are studied. The parameter of both algorithms determines the effectiveness of the bias reduction and should be chosen large enough to ensure bias-free images. This means that both algorithms become more similar to least squares algorithms, which turned out to be necessary to obtain bias-free reconstructions. This comes at the cost of increased variance. Nevertheless, NEGML and AML have lower variance than FBP. Furthermore, randoms handling has a large influence on the bias. Reconstruction with smoothed randoms results in lower bias compared to reconstruction with unsmoothed randoms or randoms precorrected data. However, NEGML and AML yield both bias-free images for large values of their parameter.

This paper presents an extension to a recent intensity-limiting sinogram completion-based Metal Artefact Reduction (MAR) algorithm for Computed Tomography (CT) images containing multiple metal objects. A novel weighting scheme is introduced, whereby the intensities of the MAR-corrected pixels are modified based on their spatial locations relative to the metal objects. Pixels falling within the straight-line regions connecting multiple metal objects are subjected to less intensive intensity-limiting, thereby compensating for the characteristic dark bands occurring in these regions. Extensive experimentation is performed on a state-of-the-art numerical simulation, a clinical CT data set and a baggage security CT data set. Comprehensive performance analysis, using reference and reference-free error metrics, Bland-Altman plots and visual comparisons, demonstrate an improvement in the restoration of the underestimated intensities occurring in the regions connecting multiple metal objects.