PatchDenoiser: 一种参数高效、多尺度补丁学习与融合的低剂量CT图像去噪器
PatchDenoiser: Parameter-efficient multi-scale patch learning and fusion denoiser for Low-dose CT imaging
AI总结：
PatchDenoiser通过多尺度补丁学习与融合，提供轻量高效且鲁棒的低剂量CT图像去噪方法，显著提升去噪性能并降低计算成本。
摘要：
低剂量CT图像对于减少癌症筛查、儿童成像和长期监测协议中的辐射暴露至关重要，但其质量常因低剂量采集、患者运动或扫描仪限制而受到噪声的破坏，影响临床解读和后续分析。传统滤波方法往往过度平滑并丢失精细解剖细节，而深度学习方法，包括CNN、GAN和Transformer，可能难以保留这些细节或需要大规模、计算成本高的模型，限制了临床实用性。我们提出了PatchDenoiser，一种轻量、能耗高效的多尺度补丁基于去噪框架。它将去噪分解为局部纹理提取和全局上下文聚合，并通过空间感知的补丁融合策略融合。这种设计使在有效抑制噪声的同时保留精细的结构和解剖细节。PatchDenoiser超轻量，参数和计算复杂度远低于CNN、GAN和基于Transformer的去噪器。在2016年梅奥低剂量CT数据集上，PatchDenoiser在PSNR和SSIM上一致优于最先进的CNN和GAN方法。它对切片厚度、重建内核和HU窗口的变化具有鲁棒性，无需微调即可跨扫描仪泛化，并将参数减少约9倍，每推理的能耗减少约27倍，相比传统CNN去噪器。因此，PatchDenoiser提供了一种实用、可扩展且计算高效的医学图像去噪解决方案，平衡了性能、鲁棒性和临床部署性。
英文摘要：
Low-dose CT images are essential for reducing radiation exposure in cancer screening, pediatric imaging, and longitudinal monitoring protocols, 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
