Tomosynthesis is a cutting-edge medical imaging technique that utilizes multiple X-ray projections to create high-resolution, three-dimensional images. Conversely, sinograms constitute two-dimensional representations derived from the scanning process, which concentrate the largest amount of information but suffer from noise and low contrast, complicating accurate interpretation. This study compares two methodologies for enhancing sinograms: a traditional histogram-based contrast adjustment method and a more innovative contrast enhancement approach utilizing a Particle Swarm Optimization (PSO) algorithm. The conventional method focuses on adjusting contrast and smoothing images to mitigate noise. At the same time, the PSO approach aims to identify optimal parameters for a transformation function that effectively enhances contrast. Both techniques were rigorously evaluated using established metrics for contrast enhancement, including Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Feature Similarity Index Measure (FSIM). The findings reveal that, although the traditional method performs adequately for simpler images, the approach based on PSO significantly enhances the quality of more complex sinograms, particularly those impacted by artifacts and elevated noise levels. This research thoroughly examines current methodologies for improving sinogram quality, highlighting their potential applications in medical diagnostics and advancements in imaging technology.