Optimization of CT Image Quality Assessment Metric Based on Genetic Algorithm

Abstract

Image Quality Assessment (IQA) metrics play an important role in helping measure computed tomography (CT) image reconstruction algorithms and trade-off dose reduction and imaging quality. However, some studies have shown that the results of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), which are commonly used IQA metrics are often inconsistent with the subjective opinions of radiologists when applied to medical images, and it is unknown whether other IQA algorithms which outperform PSNR and SSIM on natural images are equally good on CT images. In this paper, we explore the performance of nine IQA metrics on CT images by comparing their running time and their correlation with mean opinion score (MOS). Moreover, inspired by the two-step framework of traditional IQA metrics, we propose a combination IQA mdoel based on genetic algorithm to optimize the existing metrics from comparative features and pooling strategies. The obtained results show that most traditional FR-IQA metrics suffer from unsatisfactory accuracy or high computational cost, while the proposed method achieves superior performance in both aspects.