Semantic contrastive learning for orthogonal X-ray computed tomography reconstruction

X-ray computed tomography (CT) is widely used in medical imaging, with sparse-view reconstruction offering an effective way to reduce radiation dose. However, ill-posed conditions often result in severe streak artifacts. Recent advances in deep learning-based methods have improved reconstruction quality, but challenges still remain. To address these challenges, we propose a novel semantic feature contrastive learning loss function that evaluates semantic similarity in high-level latent spaces and anatomical similarity in shallow latent spaces. Our approach utilizes a three-stage U-Net-based architecture: one for coarse reconstruction, one for detail refinement, and one for semantic similarity measurement. Tests on a chest dataset with orthogonal projections demonstrate that our method achieves superior reconstruction quality and faster processing compared to other algorithms. The results show significant improvements in image quality while maintaining low computational complexity, making it a practical solution for orthogonal CT reconstruction.

CT-Mamba: A Hybrid Convolutional State Space Model for Low-Dose CT Denoising

Low-dose CT (LDCT) significantly reduces the radiation dose received by patients, thereby decreasing potential health risks. However, dose reduction introduces additional noise and artifacts, adversely affecting image quality and clinical diagnosis. Currently, denoising methods based on convolutional neural networks (CNNs) face limitations in long-range modeling capabilities, while Transformer-based denoising methods, although capable of powerful long-range modeling, suffer from high computational complexity. Furthermore, the denoised images predicted by deep learning-based techniques inevitably exhibit differences in noise distribution compared to Normal-dose CT (NDCT) images, which can also impact the final image quality and diagnostic outcomes. In recent years, the feasibility of applying deep learning methods to low-dose CT imaging has been demonstrated, leading to significant achievements. This paper proposes CT-Mamba, a hybrid convolutional State Space Model for LDCT image denoising. The model combines the local feature extraction advantages of CNNs with Mamba's global modeling capability, enabling it to capture both local details and global context. Additionally, a Mamba-driven deep noise power spectrum (NPS) loss function was designed to guide model training, ensuring that the noise texture of the denoised LDCT images closely resembles that of NDCT images, thereby enhancing overall image quality and diagnostic value. Experimental results have demonstrated that CT-Mamba performs excellently in reducing noise in LDCT images, enhancing detail preservation, and optimizing noise texture distribution, while demonstrating statistically similar radiomics features to those of NDCT images (p > 0.05). The proposed CT-Mamba demonstrates outstanding performance in LDCT denoising and holds promise as a representative approach for applying the Mamba framework to LDCT denoising tasks.