DenoMamba: A Fused State-Space Model for Low-Dose CT Denoising
Şaban Öztürk, Oğuz Can Duran, Tolga Çukur
Abstract
Low-dose computed tomography (LDCT) lowers risks linked to radiation exposure, but relies on advanced denoising algorithms to maintain diagnostic image quality. Reigning learning-based models aim to separate noise from tissue signals by projecting LDCT images through multiple network stages that extract latent feature maps. Naturally, separation fidelity depends on the model's ability to capture short- to long-range contextual dependencies across spatial and channel dimensions of these maps. Existing convolutional and transformer models either lack sensitivity to long-range context or suffer from efficiency-related trade-offs, limiting their effectiveness. To achieve high-fidelity LDCT denoising, here we introduce a novel denoising method, DenoMamba, that performs state-space modeling (SSM) to efficiently capture both short- and long-range context in CT images. DenoMamba leverages a novel cascaded architecture equipped with spatial SSM modules to encode spatial context and channel SSM modules comprising a gated convolution network to encode content-aware features of channel context. Contextual feature maps are then consolidated with low-level spatial features via a convolution fusion module (CFM). Comprehensive experiments at 25% and 10% dose reduction demonstrate that DenoMamba outperforms state-of-the-art convolutional, transformer and SSM denoisers with average improvements of 1.6 dB PSNR and 1.7% SSIM in image quality.