发表机构
Sano Centre for Computational Medicine; AGH University of Krakow; Jagiellonian University; Wroclaw University of Science and Technology（萨诺计算医学中心; AGH科技大学; 雅盖隆大学; 弗罗茨瓦夫理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对CT重建核选择中分辨率与噪声的权衡，提出BEMD-QBF框架，将锐利核图像转换为平滑核表征，在多种核上实现更优SSIM和PSNR，无需原始投影数据。
AI 中文摘要
计算机断层扫描（CT）图像的质量受到重建核选择的显著影响：锐利核可提高空间分辨率但会增加噪声，而平滑核在降低噪声的同时却牺牲了边缘清晰度。本研究提出了一种创新的增强框架，利用二维经验模态分解结合四元数双边滤波（BEMD--QBF），将锐利核CT图像转换为类似平滑核的表征，同时保持关键的解剖结构。该技术通过BEMD将每幅图像分解为固有模态函数，并在一个连贯的四元数框架内对其进行分析，以实现有效的噪声抑制和结构完整性。所提出的方法使用多种重建核（B50、B46、B41、B36、B35、B31）进行评估，并与公认的滤波策略进行比较，包括非局部均值、各向异性扩散、双边滤波和四元数双边滤波。结构相似性指数（SSIM）和峰值信噪比（PSNR）的定量评估表明，BEMD-QBF在所有评估的核上均持续获得优越的结构保真度和具有竞争力的噪声抑制效果。结果强调了所提出策略作为一种可行方法的有效性，可在无需访问原始投影数据的情况下增强重建后的CT图像，获得更优的图像质量。
英文摘要
The quality of computed tomography (CT) images is significantly affected by the selection of reconstruction kernels: sharp kernels improve spatial resolution but increase noise, whereas soft kernels diminish noise at the expense of edge clarity. This study presents an innovative enhancement framework utilising Bidimensional Empirical Mode Decomposition in conjunction with Quaternion Bilateral Filtering (BEMD--QBF) to convert sharp-kernel CT images into representations resembling soft-kernels, while maintaining critical anatomical structures. The technique disaggregates each image into intrinsic mode functions via BEMD and analyzes them inside a cohesive quaternion framework to attain efficient noise reduction and structural integrity. The proposed methodology is evaluated using several reconstruction kernels (B50, B46, B41, B36, B35, B31) and compared with recognised filtering strategies, including Non-Local Means, Anisotropic Diffusion, Bilateral Filtering, and Quaternion Bilateral Filtering. Quantitative evaluations of the Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) indicate that BEMD-QBF consistently attains superior structural fidelity and competitive noise reduction across all evaluated kernels. The results underscore the efficacy of the proposed strategy as a viable approach to enhancing post-reconstruction CT images, yielding superior image quality without requiring access to raw projection data.
DOI：10.1007/978-3-032-36871-3_22
