Computed Tomography (CT) is a widely used medical imaging modality, and as it is based on ionizing radiation, it is desirable to minimize the radiation dose. However, a reduced radiation dose comes with reduced image quality, and reconstruction from low-dose CT (LDCT) data is still a challenging task which is subject to research. According to the LoDoPaB-CT benchmark, a benchmark for LDCT reconstruction, many state-of-the-art methods use pipelines involving UNet-type architectures. Specifically the top ranking method, ItNet, employs a three-stage process involving filtered backprojection (FBP), a UNet trained on CT data, and an iterative refinement step. In this paper, we propose a less complex two-stage method. The first stage also employs FBP, while the novelty lies in the training strategy for the second stage, characterized as the CT image enhancement stage. The crucial point of our approach is that the neural network is pretrained on a distinctly different pretraining task with non-CT data, namely Gaussian noise removal on a variety of natural grayscale images (photographs). We then fine-tune this network for the downstream task of CT image enhancement using pairs of LDCT images and corresponding normal-dose CT images (NDCT). Despite being notably simpler than the state-of-the-art, as the pretraining did not depend on domain-specific CT data and no further iterative refinement step was necessary, the proposed two-stage method achieves competitive results. The proposed method achieves a shared top ranking in the LoDoPaB-CT challenge and a first position with respect to the SSIM metric.

翻译：计算机断层扫描（CT）是一种广泛使用的医学成像模态。由于基于电离辐射，因此尽可能降低辐射剂量是必要的。然而，辐射剂量降低会导致图像质量下降，从低剂量CT（LDCT）数据中重建图像仍是一项具有挑战性的研究课题。根据LDCT重建基准LoDoPaB-CT，许多前沿方法采用基于UNet架构的流水线。具体而言，排名最高方法ItNet采用三阶段流程：滤波反投影（FBP）、基于CT数据训练的UNet，以及迭代优化步骤。本文提出了一种更简单的两阶段方法。第一阶段同样采用FBP，而创新之处在于第二阶段（即CT图像增强阶段）的训练策略。该方法的核心在于：神经网络首先在截然不同的预训练任务（即在多种自然灰度图像（照片）上进行高斯噪声去除）中使用非CT数据进行预训练。随后，我们利用LDCT图像与对应常规剂量CT（NDCT）图像配对，针对CT图像增强这一下游任务对网络进行微调。尽管该方法明显比现有技术更简单（预训练不依赖于特定领域的CT数据，且无需后续迭代优化步骤），但所提出的两阶段方法仍取得了具有竞争力的结果。本方法在LoDoPaB-CT挑战中达到共享排名第一的成绩，并在SSIM指标上位列第一。