A comparative study of artifact reduction techniques in metal-implanted CT scans.

Over the past few decades, X-ray computed tomography (CT) has been introduced as one of the main cross-sectional imaging methods in a wide range of clinical applications in diagnostic radiology, oncology, and multimodality molecular imaging. Despite the acknowledged value of this imaging method, in some cases, the quality of CT images is affected by the presence of metallic implants. The presence of metal objects such as dental fillings, hip or knee prostheses, pacemakers, war shrapnel, and spinal cages cause and exacerbate image artifacts. These types of artifacts appear in the image as black and white lines that obscure the structures and tissues surrounding the metal implant and destroy the diagnostic value of CT images. These artifacts also affect the accuracy of radiotherapy treatment planning, which relies on CT images to characterize electron density and estimate the relative stopping power of particles. Therefore, to solve this problem, over the past 4 decades, algorithms called Metal Artifact Reduction (MAR) have been proposed. The objective of this study was to assess the five MAR algorithms using simulation and clinical studies. The algorithms include linear interpolation (LI-MAR) of degraded data in sinograms, normalization metal artifact reduction (NMAR), metal removal method (MDT), metal artifact reducer for orthopedic implants (OMAR), and a method based on iteration-based algorithms (MAP). Clinical images in different body regions, with different dimensions and types of metal implants, have been studied to evaluate the performance of MAR algorithms. To quantitatively assess the quality of images modified with MAR algorithms, the normalized root mean square error (NRMSE) criterion has been calculated and evaluated. The results of the algorithm evaluation showed that the NMAR algorithm was more efficient than other algorithms in reducing metal artifacts in most cases. Also, the algorithm processing time parameter demonstrated the clinical value of the NMAR algorithm.