Application of Fast Non-Local Denoising Approach in Digital Radiography Using Lung Nodule Phantom for Radiation Dose Reduction

Abstract

Introduction: Chest X-ray imaging has become the most commonly used, as it is the primary method for lung cancer screening during medical check-ups. The radiation dose should be minimized to ensure that the patients are not overexposed to radiation. However, radiation dose reduction results in increased noise in the chest X-ray image. Thus, the purpose of this study was to evaluate the utility of fast non-local means (FNLM) filters to reduce radiation dose while maintaining sufficient image quality.Material and Methods: This study evaluates three filters (median, Wiener, and total variation) and a newly proposed filter (fast non-local means (FNLM)), which reduce image noise. A realistic anthropomorphic phantom is used to compare images acquired depending on positions such as anterior-posterior, lateral, and posterior-anterior, using a self-produced 3D printed lung nodule phantom. To evaluate image quality, we used the normalized noise power spectrum (NNPS), contrast to noise ratio (CNR), and coefficient of variation (COV) evaluation parameters.Results: The NNPS and COV were lowest and the CNR was highest with FNLM images. FNLM filter outperforms other compared filters in terms of noise reduction.Conclusion: Therefore, the use of an FNLM filter is recommended, because it reduces the radiation dose to a patient and thus minimizes the risk of cancer, while maintaining diagnostic quality.

Keywords

• digital radiography x
• ray image denoising fast non
• local means (fnlm) approach 3d printing quantitative evaluation of image quality