Annually, over two million CT-scans are acquired in the Netherlands. Metal implants such as orthopedic devices, surgical clips, and pacemakers are present in approximately twenty percent of the patients that undergo a CT-scan. These metal implants introduce metal artifacts that degrade image quality, hindering assessment of CT-images. Traditional techniques like virtual monoenergetic images (monoE) and metal artifact reduction algorithms such as orthopedic metal artifact reduction (O-MAR) have been developed over the past years. Newer technologies like photon-counting CT (PCCT) and artificial intelligence (AI) offer promising opportunities for metal artifact reduction. In this thesis we investigated the efficacy of these traditional and novel metal artifact reduction techniques. While monoE showed to be effective in reducing mild artifacts, more severe metal artifacts can be reduced using O-MAR. However, caution is required using O-MAR as secondary artifacts may be introduced. PCCT allows for the reconstruction of energy bins in addition to monoE, which are capable of reducing metal artifacts. Despite advancements, no single technique effectively addresses all implant types. To address this, a deep learning-based metal artifact reduction algorithm (DL-MAR) was developed, showing fewer artifacts, higher image quality and higher diagnostic confidence compared to traditional methods in patient studies. However, widespread clinical implementation of DL-MAR may take time.To aid current clinical practice, a comprehensive overview of metal artifact reduction strategies and recommendations based on implant types was provided. While these recommendations may not be optimal for each individual patient, they offer guidance to facilitate clinical implementation of available metal artifact reduction techniques
