Correction of Cupping Artifacts in Axial Cone-Beam Computed Tomography Images by Using Image Processing Algorithms

Objectives: One of the most important problems of cone-beam computed tomography (CBCT) imaging technique is the presence of dense objects, such as implants, amalgam fillings, and metal veneers, which result in beam-hardening artifacts. With an increase in the application of CBCT images and considering the problems in relation to cupping artifacts, some algorithms have been presented to reduce these artifacts. The aim was to present an algorithm to eliminate cupping artifacts from axial and other reconstructed CBCT images. Materials and Methods: We used CBCT images of NewTom VG imaging system (Verona, Italy, at Dentistry Faculty, Medical Sciences University, Tabriz, Iran) in which every image has a resolution of 366 × 320 in DICOM format. 50 images of patients with cupping artifacts were selected. Using Sobel edge detector and nonlinear gamma correction coefficient, the difference was calculated between the density of axial images in the main image and the image resulting from nonlinear gamma correction at the exact location of the radiopaque dental materials detected by Sobel. The points at which this density difference was out of a definite limit were treated as image artifacts and were eliminated from the main image by the inpainting method. Results: The resultant axial images, for producing reconstructed cross-sectional, panoramic images without cupping artifacts, were imported into NTT viewer V5.6 and utilized. Conclusions: With comparison, acquired images observed that the offering algorithm is practical and effective for reducing the cupping artifacts and preserving the quality of the reconstructed images. This algorithm does not need any additional equipment.

Polychromatic CT Data Improvement with One-Parameter Power Correction

Standard approaches to tomography reconstruction of the projection data registered with polychromatic emission lead to the appearance of cupping artifacts and irrelevant lines between regions of strong absorption. The main reason for their appearance is the fact that part of the emission with low energy is being absorbed entirely by high absorbing objects. This fact is known as beam hardening (BH). The procedure of processing projection data collected in polychromatic mode is presented; it reduces artifacts relevant to BH and does not require additional calibration experiments. The procedure consists of two steps: the first is to linearize the projection data with one-parameter power correction, and the second is to reconstruct the images from linearized data. Automatic parameter adjustment is the main advantage of the procedure. The optimization problem is formulated. The system flowchart is presented. The reconstruction with different powers of correction is considered to evaluate the quality reconstruction.

X-Ray Scatter Correction on Soft Tissue Images for Portable Cone Beam CT

Soft tissue images from portable cone beam computed tomography (CBCT) scanners can be used for diagnosis and detection of tumor, cancer, intracerebral hemorrhage, and so forth. Due to large field of view, X-ray scattering which is the main cause of artifacts degrades image quality, such as cupping artifacts, CT number inaccuracy, and low contrast, especially on soft tissue images. In this work, we propose the X-ray scatter correction method for improving soft tissue images. The X-ray scatter correction scheme to estimate X-ray scatter signals is based on the deconvolution technique using the maximum likelihood estimation maximization (MLEM) method. The scatter kernels are obtained by simulating the PMMA sheet on the Monte Carlo simulation (MCS) software. In the experiment, we used the QRM phantom to quantitatively compare with fan-beam CT (FBCT) data in terms of CT number values, contrast to noise ratio, cupping artifacts, and low contrast detectability. Moreover, the PH3 angiography phantom was also used to mimic human soft tissues in the brain. The reconstructed images with our proposed scatter correction show significant improvement on image quality. Thus the proposed scatter correction technique has high potential to detect soft tissues in the brain.