Low-Dose Computed Tomography Image Denoising Vision Transformer Model Optimization Using Space State Method

Abstract

Low-dose computed tomography (LDCT) is widely used to promote reduction of patient radiation exposure, but the associated increase in image noise poses challenges for diagnostic accuracy. In this study, we propose a Vision Transformer (ViT)-based denoising framework enhanced with a State Space Optimizing Block (SSOB) to improve both image quality and computational efficiency. The SSOB upgrades the multihead self-attention mechanism by reducing spatial redundancy and optimizing contextual feature fusion, thereby strengthening the transformer's ability to capture long-range dependencies and preserve fine anatomical structures under severe noise. Extensive evaluations on randomized and categorized datasets demonstrate that the proposed model consistently outperforms existing state-of-the-art denoising approaches. It achieved the highest average SSIM (up to 6.10% improvement), PSNR values (36.51 ± 0.37 dB on randomized and 36.30 ± 0.36 dB on categorized datasets), and the lowest RMSE, surpassing recent CNN-transformer-based denoising hybrid models by approximately 12%. Intensity profile analysis further confirmed its effectiveness, showing sharper edge transitions and more accurate gray-level distributions across anatomical boundaries, closely aligning with ground truth and retaining subtle diagnostic features often lost in competing models. In addition to improved reconstruction quality, the SSOB-empowered ViT achieved notable computational gains. It delivered the fastest inference (0.42 s per image), highest throughput (2.38 images/s), lowest GPU memory usage (750 MB), and smallest model size (7.6 MB), alongside one of the shortest training times (6.5 h). Compared to legacy architectures, which required up to 16 h of training and substantially more resources, the proposed model offers both accuracy and deployability. Collectively, these findings establish the SSOB as a key component for efficient transformer-based LDCT denoising, addressing memory and convergence challenges while preserving global contextual advantages.