Ionizing radiation has been the biggest concern in CT imaging. To reduce the dose level without compromising the image quality, low-dose CT reconstruction has been offered with the availability of compressed sensing based reconstruction methods. Recently, data-driven methods got attention with the rise of deep learning, the availability of high computational power, and big datasets. Deep learning based methods have also been used in low-dose CT reconstruction problem in different manners. Usually, the success of these methods depends on labeled data. However, recent studies showed that training can be achieved successfully with noisy datasets. In this study, we defined a training scheme to use low-dose sinograms as their own training targets. We applied the self-supervision principle in the projection domain where the noise is element-wise independent which is a requirement for self-supervised training methods. Using the self-supervised training, the filtering part of the FBP method and the parameters of a denoiser neural network are optimized. We demonstrate that our method outperforms both conventional and compressed sensing based iterative reconstruction methods qualitatively and quantitatively in the reconstruction of analytic CT phantoms and real-world CT images in low-dose CT reconstruction task.

翻译：电离辐射一直是CT成像中最受关注的问题。为在不牺牲图像质量的前提下降低辐射剂量，基于压缩感知的重建方法推动了低剂量CT重建技术的发展。近年来，随着深度学习的兴起、计算能力的提升以及大规模数据集的可用性，数据驱动方法受到广泛关注。基于深度学习的方法也以多种形式应用于低剂量CT重建问题中。通常，这些方法的成功依赖于标注数据。然而，最新研究表明，使用含噪声数据集同样可以成功完成训练。本研究提出一种训练方案，将低剂量正弦图作为其自身的训练目标。我们在投影域中应用自监督原则，该域中的噪声具有逐元素独立性，符合自监督训练方法的基本要求。通过自监督训练，我们优化了滤波反投影（FBP）方法的滤波部分以及去噪神经网络的参数。实验证明，在低剂量CT重建任务中，无论是针对解析CT体模还是真实CT图像，我们的方法在定性和定量评估上均优于传统重建方法及基于压缩感知的迭代重建方法。