GAN-Based Architecture for Low-dose Computed Tomography Imaging Denoising

TL;DR

This paper reviews recent advancements in GAN-based methods for low-dose CT image denoising, highlighting improvements, challenges, and clinical implications of these AI-driven techniques.

Contribution

It provides a comprehensive synthesis of GAN architectures and their evolution for LDCT denoising, emphasizing state-of-the-art models and clinical evaluation metrics.

Findings

GANs improve image quality in benchmark datasets

Advanced GAN models incorporate anatomical priors and perceptual loss

Challenges include interpretability and artifact management

Abstract

Generative Adversarial Networks (GANs) have surfaced as a revolutionary element within the domain of low-dose computed tomography (LDCT) imaging, providing an advanced resolution to the enduring issue of reconciling radiation exposure with image quality. This comprehensive review synthesizes the rapid advancements in GAN-based LDCT denoising techniques, examining the evolution from foundational architectures to state-of-the-art models incorporating advanced features such as anatomical priors, perceptual loss functions, and innovative regularization strategies. We critically analyze various GAN architectures, including conditional GANs (cGANs), CycleGANs, and Super-Resolution GANs (SRGANs), elucidating their unique strengths and limitations in the context of LDCT denoising. The evaluation provides both qualitative and quantitative results related to the improvements in performance in benchmark and clinical datasets with metrics such as PSNR, SSIM, and LPIPS. After highlighting the positive results, we discuss some of the challenges preventing a wider clinical use, including the interpretability of the images generated by GANs, synthetic artifacts, and the need for clinically relevant metrics. The review concludes by highlighting the essential significance of GAN-based methodologies in the progression of precision medicine via tailored LDCT denoising models, underlining the transformative possibilities presented by artificial intelligence within contemporary radiological practice.