Enabling Predication of the Deep Learning Algorithms for Low-Dose CT Scan Image Denoising Models : A Systematic Literature Review

Abstract

Computed Tomography (CT) is a non-invasive imaging modality used to detect abnormalities in the human body with high precision. However, the electromagnetic radiation emitted during CT scans poses health risks, potentially leading to the development of metabolic abnormalities and genetic disorders, which increase the risk of cancer. The Low-Dose CT (LDCT) scanning technique was developed to address these hazards, but it has several limitations, including noise, artifacts, reduced contrast, and structural changes. These drawbacks significantly reduce the diagnostic capabilities of Computer-Aided Diagnosis (CAD) systems. Eliminating these noises and artifacts while preserving critical features poses a significant challenge. Traditional CT denoising algorithms struggle with edge blurring and high computational costs, often generating artifacts in flat regions as noise levels increase. Consequently, deep learning-based methods have emerged as a promising solution for LDCT image denoising. In this study, a comprehensive Systematic Literature Review (SLR) following PRISMA guidelines was conducted to explore the latest advancements in deep learning algorithms for LDCT image denoising. This SLR spans LDCT image-denoising research from 2018 to 2024, providing a detailed summary of methodologies, benefits, limitations, parameters, and trends. This study delves into the acquisition process of CT scans, investigating radiation absorption across various anatomical regions, as well as identifying sources of noise and its distribution within the LDCT images. Additionally, it enhances our understanding of LDCT image denoising trends and provides valuable insights for future research, thus making a substantial contribution to ongoing efforts to enhance the quality and reliability of LDCT images.