Low-dose computed tomography (LDCT) has become the technology of choice for diagnostic medical imaging, given its lower radiation dose compared to standard CT, despite increasing image noise and potentially affecting diagnostic accuracy. To address this, advanced deep learning-based LDCT denoising algorithms have been developed, primarily using Convolutional Neural Networks (CNNs) or Transformer Networks with the Unet architecture. This architecture enhances image detail by integrating feature maps from the encoder and decoder via skip connections. However, current methods often overlook enhancements to the Unet architecture itself, focusing instead on optimizing encoder and decoder structures. This approach can be problematic due to the significant differences in feature map characteristics between the encoder and decoder, where simple fusion strategies may not effectively reconstruct images.In this paper, we introduce WiTUnet, a novel LDCT image denoising method that utilizes nested, dense skip pathways instead of traditional skip connections to improve feature integration. WiTUnet also incorporates a windowed Transformer structure to process images in smaller, non-overlapping segments, reducing computational load. Additionally, the integration of a Local Image Perception Enhancement (LiPe) module in both the encoder and decoder replaces the standard multi-layer perceptron (MLP) in Transformers, enhancing local feature capture and representation. Through extensive experimental comparisons, WiTUnet has demonstrated superior performance over existing methods in key metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and Root Mean Square Error (RMSE), significantly improving noise removal and image quality.

翻译：低剂量计算机断层扫描（LDCT）因其相较于标准CT更低的辐射剂量，已成为诊断医学成像的首选技术，但图像噪声增加可能影响诊断准确性。为解决此问题，先进基于深度学习的LDCT去噪算法已得到发展，主要采用卷积神经网络（CNN）或Transformer网络结合Unet架构。该架构通过跳跃连接集成编码器和解码器的特征图以增强图像细节。然而，现有方法常忽视对Unet架构本身的改进，转而专注于优化编码器和解码器结构。这种做法可能存在问题，因为编码器与解码器的特征图特性存在显著差异，简单的融合策略可能无法有效重建图像。本文提出WiTUnet，一种新颖的LDCT图像去噪方法，采用嵌套密集跳跃路径替代传统跳跃连接以改善特征集成。WiTUnet还引入窗口化Transformer结构，以不重叠的小块处理图像，降低计算负荷。此外，在编码器和解码器中集成局部图像感知增强（LiPe）模块，替代Transformer中的标准多层感知器（MLP），增强局部特征捕获与表示。通过广泛实验比较，WiTUnet在峰值信噪比（PSNR）、结构相似性（SSIM）和均方根误差（RMSE）等关键指标上均展现出优于现有方法的性能，显著提升了噪声去除与图像质量。