A Novel Attention-Guided Enhanced U-Net With Hybrid Edge-Preserving Structural Loss for Low-Dose CT Image Denoising
Muhammad Zubair, Helmi Md Rais, Talal Alazemi
Abstract
Computed Tomography (CT) scan, pivotal for medical diagnostics, involves exposure to electromagnetic radiation, potentially elevating the risk of leukemia and cancer. Low-dose CT (LDCT) imaging has emerged to mitigate these risks, extensively reducing radiation exposure by up to 86%. However, it significantly reduces the quality of LDCT images and introduces noise and artifacts, degrading the diagnostic accuracy of the Computer Aided Diagnostic (CAD) system. This study presents a novel U-Net architecture, featuring several key enhancements. The model integrates residual blocks to improve feature representation and employs a custom hybrid loss function that combines structural loss with gradient regularization using the Euclidean norm, promoting superior CT image quality retention. Additionally, incorporating Attention Gates in the up-sampling layers of a proposed model optimizes the extraction of critical features, ensuring more precise denoising of CT images. The proposed model undergoes iterative training, using a custom loss function to refine its parameters and improve CT image denoising progressively. Its performance is rigorously evaluated both qualitatively and quantitatively on the ’2016 Low-dose CT AAPM Grand Challenge dataset’. The results, assessed through the metrics Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Root Mean Square Error (RMSE), demonstrated promising improvements compared to state-of-the-art techniques. The model effectively reduces noise while preserving critical fine details, establishing itself as a highly efficient solution for LDCT image denoising.