X-ray CT high-density artefact suppression in the presence of bones

Abstract

This paper presents a novel method of reducing x-ray CT high-density artefacts generated by metal objects when abundant bone structures are present in the region of interest. This method has an advantage over previously proposed methods since it heavily suppresses the metal artefacts without introducing extra bone artefacts. The method of suppression requires that bone pixels are isolated and segmented by thresholding. Then artificial CT numbers are assigned to the bone pixels so that their projection profiles are smooth and thus can be properly simulated by a polynomial interpolation. The projection profile of the metal object is then removed to fully suppress the artefacts. The resulting processed profile is fed to a reconstruction routine and the previously preserved bone pixels added back. The new method utilizes two important features of the CT image with metal artefacts: (a) metal and bone pixels are not severely affected by the high-density artefacts and (b) the high-density artefacts can be located in specific projection channels in the profile domain, although they are spread out in the image domain. This suppression method solves the problem of CT image artefacts arising from metal objects in the body. It has the potential to greatly improve diagnostic CT imaging in the presence of these objects and treatment planning that utilizes CT for patients with metal applicators (e.g., brachytherapy for cervix cancer and prostate cryotherapy).

Comparison of two cone beam computed tomography multiplanar reconstruction orientation protocols

“…Other factors that lead to potential measurement errors are streak and star artifacts in the reconstructed CT/ CBCT images from metal implants [38][39][40] . In our sample, the images were prescreened to include scans which did not demonstrate large metal artifacts.…”

X-ray CT Metal Artifact Reduction Using Wavelet Domain <formula formulatype="inline"><tex Notation="TeX">$L_{0}$</tex></formula> Sparse Regularization

“…For every , the minimum energy of the envelope of is achieved at a unique point, , called proximal map or proximum, i.e., ( [49]) (8) By this definition, the DRS algorithm iteratively estimates the minimizer of the problem (7) as follows: (9) (10) where and are the proximity operators of the functions and is a (variable) relaxation parameter in the range (0, 2) and and are two sequences associated with the proximity operators allowing for approximations in their calculation and inexact implementation. The existence and uniqueness conditions of a solution to the optimization problem (7) using the DRS algorithm are discussed in [48].…”

“…In the projection completion approach, which is often followed by filtered-back-projection (FBP) image reconstruction, the missing projections are synthesized from neighboring projections using linear and polynomial interpolation [4], [8]- [10], wavelet interpolation [11], adaptive filtering [12], [13], and inpainting [14]- [17] techniques. Other approaches aim at replacing the missing projections with those available in opposite view angles or adjacent CT slices [18]- [20] or the projections obtained from forward projection of tissue-segmented CT images [21]- [24].…”

Volumetric deformable face models for cranio-facial reconstruction

“…Unfortunately, many of the images contain heavy streak artifacts because of the amalgam teeth fillings. These artifacts are reduced using a simplified version of a projection interpolation method [17]. First, pixels related to high density material are identified using intensity thresholding at 3000 HU.…”

“…Unfortunately, metallic teeth filling often disturb the image quality, leading to errors in the hard and soft tissue segmentations. These artifacts can be minimized by proper metal artefact reduction techniques [17].…”