Computed tomography (CT) has played a vital role in medical diagnosis, assessment, and therapy planning, etc. In clinical practice, concerns about the increase of x-ray radiation exposure attract more and more attention. To lower the x-ray radiation, low-dose CT (LDCT) has been widely adopted in certain scenarios, while it will induce the degradation of CT image quality. In this paper, we proposed a deep learning-based method that can train denoising neural networks without any clean data.

中文翻译：

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Noise2Context：用于低剂量 CT 的上下文辅助学习 3D 薄层

计算机断层扫描 (CT) 在医学诊断、评估和治疗计划等方面发挥了至关重要的作用。在临床实践中，对 X 射线辐射暴露增加的担忧越来越受到关注。为了降低X射线辐射，在某些场景中广泛采用低剂量CT（LDCT），但会导致CT图像质量下降。在本文中，我们提出了一种基于深度学习的方法，可以在没有任何干净数据的情况下训练去噪神经网络。