NLE-ANSNet: A Multilevel Noise Estimation and Adaptive Scaling Framework for Hybrid Noise Suppression in Contrast-Enhanced Magnetic Resonance Imaging for Hepatocellular Carcinoma

Jasem Almotiri ()
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Jasem Almotiri: Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia

Mathematics, 2025, vol. 13, issue 11, 1-36

Abstract: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, so its detection and monitoring are critical. However, contrast-enhanced magnetic resonance imaging (CE-MRI) is particularly vulnerable to complex, unstructured noise, which compromises image quality and diagnostic accuracy. This study proposes the use of NLE-ANSNet, a deep learning-based denoizing framework that integrates multilevel noise level estimators (NLEs) and adaptive noise scaling (ANS) within residual blocks. The model performs progressive, stagewise noise suppression at multiple feature depths, dynamically adjusting normalization based on localized noise estimates. This enables context-aware denoizing, preserving fine anatomical details. To simulate clinically realistic conditions, we developed a hybrid noise simulation framework that combines Gaussian, Poisson, and Rician noise at the pixel level. This framework aims to approximate a balanced noise distribution for evaluation purposes, with both mean and median noise levels reported to enhance evaluation robustness and prevent bias from extreme cases. NLE-ANSNet achieves a PSNR of 34.01 dB and an SSIM of 0.9393, surpassing those of state-of-the-art models. The method aims to support diagnostic reliability by preserving image structure and intensity fidelity in CE-MRI interpretation. In addition to quantitative analysis, a qualitative assessment was conducted to visually compare denoizing outputs across models, further demonstrating NLE-ANSNet’s superior ability to suppress noise while preserving diagnostically critical information. Unlike previous approaches, this study introduces a denoizing framework that combines multilevel noise estimation and adaptive noise scaling specifically tailored for CE-MRI in HCC under hybrid noise conditions—a clinically relevant and underexplored area. Overall, this study supports improved clinical decision making in HCC management.

Keywords: denoizing; noise reduction; deep learning; medical image noise; image restoration; image quality; MRI; hepatocellular carcinoma; CE-MRI (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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