MAGNETIC RESONANCE IMAGE DENOIZING USING A DUAL-CHANNEL DISCRIMINATIVE DENOIZING NETWORK

Abstract

Magnetic Resonance Imaging (MRI) provides detailed information about soft tissues, which is essential for disease analysis. However, the presence of Rician noise in MR images introduces uncertainties that challenge medical practitioners during analysis. The objective of our research paper is to introduce an innovative dual-channel deep learning (DL) model designed to effectively denoize MR images. The methodology of this model integrates two distinct pathways, each equipped with unique normalization and activation techniques, facilitating the creation of a wide range of image features. Specifically, we employ Group Normalization in combination with Parametric Rectified Linear Units (PRELU) and Local Response Normalizations (LRN) alongside Scaled Exponential Linear Units (SELU) within both channels of our denoizing network. The outcomes of our proposed network exhibit clinical relevance, empowering medical professionals to conduct more efficient disease analysis. When evaluated by experienced radiologists, our results were deemed satisfactory. The network achieved a noteworthy improvement in performance metrics without requiring retraining. Specifically, there was a (6.65 ± 0.03)% enhancement in Peak Signal-to-Noise Ratio (PSNR) values and a (3.9 ± 0.02)% improvement in Structural Similarity Index (SSIM) values. Furthermore, when evaluated on the dataset on which the network was initially trained, the increase in PSNR and SSIM values was even more pronounced, with a (7.5 ± 0.02)% improvement in PSNR and a (4.3 ± 0.01)% enhancement in SSIM. Evaluation metrics, such as SSIM and PSNR, demonstrated a notable enhancement in the results obtained using our network. The statistical significance of our findings is evident, with p-values consistently less than 0.05 (p < 0.05). Importantly, our network demonstrates exceptional generalizability, as it performs remarkably well on different datasets without the need for retraining.