Comparative Evaluation of Metal Artifact Reduction Algorithms in Computed Tomography: Siemens iMAR vs. GE MARS for Improved Radiotherapy Planning

Abstract Introduction In this article, the effectiveness of two commercial metal artifact reduction algorithms, Siemens iMAR and GE MARS, in computed tomography (CT) imaging is evaluated. Metal artifacts, which arise primarily due to the presence of high atomic number metals in clinical imaging, significantly degrade image quality and impede accurate diagnostics. Material and methods The study compares monoenergetic and dual-energy CT reconstruction algorithms by examining their performance on phantom models, including a Gammex Tissue Characterization Phantom and a custom-made spine stabilization system phantom. Quantitative assessments, such as Hounsfield unit analysis were performed. Results The results show that the iterative reconstruction algorithm (iMAR) from Siemens offers superior artifact suppression and image clarity compared to GE’s dual-energy algorithm (MARS), particularly in cases involving titanium implants. Quantitative assessments, such as Hounsfield unit measurements and visual image analysis, confirm that iMAR produces images with reduced artifacts and more consistent tissue characterization. Conclusions These findings suggest that the choice of artifact reduction algorithm has a profound impact on the diagnostic and planning accuracy of CT scans in patients with metal implants.