Metaheuristic-optimized generative adversarial network for enhanced sparse-view low-dose CT reconstruction

Sparse-view low-dose computed tomography (LDCT) imaging poses difficulties in preserving image quality while reducing radiation exposure. Recent research has focused extensively on artificial intelligence (AI) to reduce artifacts in LDCT. This paper presents a unique integration based on a conditional generative adversarial network (CGAN) with metaheuristic optimization to improve the reconstruction of sparse-view computed tomography (CT) images. A Pix2Pix CGAN-based model was integrated with Particle Swarm Optimization (PSO), Differential Evolution (DE), and Cuckoo Search (CS) to improve essential hyperparameters, such as learning rate and beta values. The LDCT-P and LUNA16 datasets were used, producing seven levels of sparse-view CT images (10, 16, 32, 64, 128, 256, and 512 views) for assessment. The findings indicated a substantial improvement in image quality with an increase in the number of view projections. Pix2Pix + PSO demonstrated superior performance, with the Structural Similarity Index metric (SSIM) rising from 0.900 (10 views) to 0.972 (512 views) for abdominal CT and from 0.801 to 0.971 for lung CT, respectively. The results underscore the capability of the Pix2Pix model integrated with metaheuristic optimization to enhance sparse-view CT reconstruction. This method adeptly reconciles computing economy with image integrity, enabling improved LDCT imaging applications in clinical settings.