Advanced Strategies for Mitigation and Quantification of Metal Artifacts in Computed Tomography: A Narrative Review

Abstract

The increasing prevalence of metallic implants in the general population poses significant challenges for Computed Tomography (CT), both in diagnostic imaging and in radiotherapy planning. Metal artifacts, arising from a combination of physical phenomena including beam hardening, photon starvation, scatter, and corruption of projection data, can degrade image quality to the point of rendering examinations non-diagnostic and can introduce clinically relevant errors in absorbed dose estimation. This narrative review describes the physical mechanisms underlying metal artifacts, critically analyzes the performance and limitations of the main reduction techniques (Metal Artifact Reduction, MAR), dual-energy spectral technologies (Dual-Energy CT, DECT), and emerging Photon-Counting CT (PCCT) platforms. Furthermore, deep learning–based algorithms and their potential to overcome the limitations of conventional methods are discussed. The objective is to provide medical imaging and radiotherapy professionals with an updated, clinically oriented framework for the optimal management of CT examinations in the presence of metallic materials.