Medical images are essential for diagnosis, treatment planning, and research, but their quality is often degraded by noise from low-dose acquisition, patient motion, or scanner limitations, affecting both clinical interpretation and downstream analysis. Traditional filtering approaches often over-smooth and lose fine anatomical details, while deep learning methods, including CNNs, GANs, and transformers, may struggle to preserve such details or require large, computationally expensive models, limiting clinical practicality. We propose PatchDenoiser, a lightweight, energy-efficient multi-scale patch-based denoising framework. It decomposes denoising into local texture extraction and global context aggregation, fused via a spatially aware patch fusion strategy. This design enables effective noise suppression while preserving fine structural and anatomical details. PatchDenoiser is ultra-lightweight, with far fewer parameters and lower computational complexity than CNN-, GAN-, and transformer-based denoisers. On the 2016 Mayo Low-Dose CT dataset, PatchDenoiser consistently outperforms state-of-the-art CNN- and GAN-based methods in PSNR and SSIM. It is robust to variations in slice thickness, reconstruction kernels, and HU windows, generalizes across scanners without fine-tuning, and reduces parameters by ~9x and energy consumption per inference by ~27x compared with conventional CNN denoisers. PatchDenoiser thus provides a practical, scalable, and computationally efficient solution for medical image denoising, balancing performance, robustness, and clinical deployability.

翻译：医学图像对于诊断、治疗规划和研究至关重要，但其质量常因低剂量采集、患者运动或扫描仪限制所产生的噪声而降低，影响临床判读与下游分析。传统滤波方法常过度平滑而丢失精细解剖细节，而包括CNN、GAN和Transformer在内的深度学习方法可能难以保留此类细节，或需要庞大且计算代价高昂的模型，限制了临床实用性。我们提出PatchDenoiser，一种轻量级、高能效的多尺度块状去噪框架。它将去噪任务分解为局部纹理提取与全局上下文聚合，并通过空间感知的块融合策略进行整合。该设计在有效抑制噪声的同时，保留了精细的结构与解剖细节。PatchDenoiser具有超轻量级特性，其参数量和计算复杂度远低于基于CNN、GAN和Transformer的去噪器。在2016年Mayo低剂量CT数据集上，PatchDenoiser在PSNR和SSIM指标上持续优于基于CNN和GAN的先进方法。它对切片厚度、重建核及HU窗的变化具有鲁棒性，无需微调即可跨扫描仪泛化，与传统CNN去噪器相比，参数量减少约9倍，单次推理能耗降低约27倍。因此，PatchDenoiser为医学图像去噪提供了一个兼顾性能、鲁棒性与临床可部署性的实用、可扩展且计算高效的解决方案。