Role and Impact of Method Noise on CT Image Denoising

Abstract

Background The main emphasis of this study is on the medical Computed Tomography (CT) imaging denoising technique, which plays a major role in interpreting patient illness information for medical diagnosis. CT imaging is indispensable for accurate disease diagnosis, but image quality is affected by noise and other artifacts. The primary objective is to improve the accuracy of denoising algorithms, which consequently increases early disease prediction and enhances the accuracy of diagnostic outcomes. Objective The major objective was to examine the performance of method noise-based Low-dose CT (LDCT) image denoising technique using a Convolutional Neural Network (CNN) in medical imaging. This method aims to suppress noise, improve image quality, and effectively minimize radiation exposure. This method enhances the accuracy of the denoising algorithm, enabling more precise disease diagnosis. Method noise, or residual noise, plays a major role in denoising CT images while preserving fine details and minimizing other artifacts generated during the denoising process. Method noise includes the omitted structural features and other minute artifacts, which are resolved through CNN-based denoising techniques. This approach elevates the overall imaging quality and clarity, resulting in better diagnostic accuracy. Methods The study includes a systematic, method noise-based approach to determine the performance of denoising algorithms in diagnosing various diseases from medical CT images that are often affected by Gaussian noise. It involves selecting a comprehensive dataset, applying a method noise approach using CNN, and evaluating the outcomes through quantitative measures, such as PSNR, SNR, and SSIM. This comparison aims to assess diagnostic interpretation, thereby improving the accuracy and efficacy of the method noise-based technique in CT medical imaging. Results The results illustrate the differential accuracy and performance of CT image denoising techniques when compared to standard filtering methods, as well as after the application of method noise-based denoising techniques. Implementing quantitative measures, such as PSNR, SNR, and SSIM, aims to improve healthcare diagnostics. Conclusion The study concludes that method noise-based CT image denoising algorithms effectively mitigate noise and artifacts while retaining the corners, contours, and precise details of CT images, subsequently improving the efficiency and accuracy of predicting diagnostic results.