Computed tomography from a low radiation dose (LDCT) is challenging due to high noise in the projection data. Popular approaches for LDCT image reconstruction are two-stage methods, typically consisting of the filtered backprojection (FBP) algorithm followed by a neural network for LDCT image enhancement. Two-stage methods are attractive for their simplicity and potential for computational efficiency, typically requiring only a single FBP and a neural network forward pass for inference. However, the best reconstruction quality is currently achieved by unrolled iterative methods (Learned Primal-Dual and ItNet), which are more complex and thus have a higher computational cost for training and inference. We propose a method combining the simplicity and efficiency of two-stage methods with state-of-the-art reconstruction quality. Our strategy utilizes a neural network pretrained for Gaussian noise removal from natural grayscale images, fine-tuned for LDCT image enhancement. We call this method FBP-DTSGD (Domain and Task Shifted Gaussian Denoisers) as the fine-tuning is a task shift from Gaussian denoising to enhancing LDCT images and a domain shift from natural grayscale to LDCT images. An ablation study with three different pretrained Gaussian denoisers indicates that the performance of FBP-DTSGD does not depend on a specific denoising architecture, suggesting future advancements in Gaussian denoising could benefit the method. The study also shows that pretraining on natural images enhances LDCT reconstruction quality, especially with limited training data. Notably, pretraining involves no additional cost, as existing pretrained models are used. The proposed method currently holds the top mean position in the LoDoPaB-CT challenge.

翻译：低辐射剂量计算机断层扫描（LDCT）因投影数据中的高噪声而具有挑战性。当前主流的LDCT图像重建方法为两阶段方法，通常包含滤波反投影（FBP）算法及后续用于LDCT图像增强的神经网络。两阶段方法因其简洁性和潜在的计算效率优势而备受青睐，在推理阶段通常仅需单次FBP计算和一次神经网络前向传播。然而，目前最佳重建质量由展开式迭代方法（Learned Primal-Dual与ItNet）实现，这些方法更为复杂，因而在训练和推理阶段具有更高的计算成本。本文提出一种融合两阶段方法简洁高效特性与顶尖重建质量的新方法。该策略采用在自然灰度图像高斯去噪任务上预训练的神经网络，并针对LDCT图像增强进行微调。我们将此方法命名为FBP-DTSGD（域与任务迁移高斯去噪器），因其微调过程包含从高斯去噪到LDCT图像增强的任务迁移，以及从自然灰度图像到LDCT图像的域迁移。通过对三种不同预训练高斯去噪器的消融实验表明，FBP-DTSGD的性能不依赖于特定去噪架构，这意味着未来高斯去噪技术的进步可能使本方法受益。研究还证实，基于自然图像的预训练能提升LDCT重建质量，在训练数据有限时效果尤为显著。值得注意的是，预训练过程无需额外成本，可直接使用现有预训练模型。该方法目前在LoDoPaB-CT挑战赛中保持平均排名首位。