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）因投影数据中存在高噪声而具有挑战性。当前流行的LDCT图像重建方法是两阶段方法，通常包括滤波反投影（FBP）算法和用于LDCT图像增强的神经网络。两阶段方法因其简单性和潜在的计算效率而备受青睐，通常只需一次FBP和一次神经网络前向传播即可完成推理。然而，目前最佳的重建质量是由展开式迭代方法（如Learned Primal-Dual和ItNet）实现的，这些方法更为复杂，因此在训练和推理阶段具有更高的计算成本。我们提出了一种方法，结合了两阶段方法的简洁高效性与最先进的重建质量。我们的策略利用一个在自然灰度图像上进行高斯噪声去除预训练的神经网络，并针对LDCT图像增强任务进行微调。我们将此方法称为FBP-DTSGD（域迁移与任务迁移高斯去噪器），因为微调过程涉及从高斯去噪到增强LDCT图像的任务迁移，以及从自然灰度图像到LDCT图像的域迁移。对三种不同预训练高斯去噪器的消融研究表明，FBP-DTSGD的性能并不依赖于特定的去噪架构，这表明未来高斯去噪技术的进步可能使本方法受益。研究还表明，在自然图像上的预训练能够提升LDCT重建质量，尤其是在训练数据有限的情况下。值得注意的是，预训练过程不产生额外成本，因为使用的是现有的预训练模型。所提出的方法目前在LoDoPaB-CT挑战赛中位列平均性能榜首。