CALIMAR-GAN: An unpaired mask-guided attention network for metal artifact reduction in CT scans

Abstract

High-quality computed tomography (CT) scans are essential for accurate diagnostic and therapeutic decisions, but the presence of metal objects within the body can produce distortions that lower image quality. Deep learning (DL) approaches using image-to-image translation for metal artifact reduction (MAR) show promise over traditional methods but often introduce secondary artifacts. Additionally, most rely on paired simulated data due to limited availability of real paired clinical data, restricting evaluation on clinical scans to qualitative analysis. This work presents CALIMAR-GAN, a generative adversarial network (GAN) model that employs a guided attention mechanism and the linear interpolation algorithm to reduce artifacts using unpaired simulated and clinical data for targeted artifact reduction. Quantitative evaluations on simulated images demonstrated superior performance, achieving a PSNR of 31.7, SSIM of 0.877, and Fréchet inception distance (FID) of 22.1, outperforming state-of-the-art methods. On real clinical images, CALIMAR-GAN achieved the lowest FID (32.7), validated as a valuable complement to qualitative assessments through correlation with pixel-based metrics ($r=-0.797$ with PSNR, $p<0.01$; $r=-0.767$ with MS-SSIM, $p<0.01$). This work advances DL-based artifact reduction into clinical practice with high-fidelity reconstructions that enhance diagnostic accuracy and therapeutic outcomes. Code is available at https://github.com/roberto722/calimar-gan.