Abstract：
Computed tomography (CT) denoising is a challenging task in medical imaging that has garnered considerable attention. Supervised networks require a lot of noisy-clean image pairs, which are always unavailable in clinical settings. Existing self-supervised algorithms for suppressing noise with paired noisy images have limitations, such as ignoring the residual between similar image pairs during training and insufficiently learning the spectrum information of images. In this study, we propose a Residual Image Prior Network (RIP-Net) to sufficiently model the residual between the paired similar noisy images. Our approach offers new insights into the field by addressing the limitations of existing methods. We first establish a mathematical theorem clarifying the non-equivalence between similar-image-based self-supervised learning and supervised learning. It helps us better understand the strengths and limitations of self-supervised learning. Secondly, we introduce a novel regularization term to model a low-frequency residual image prior. This can improve the accuracy and robustness of our model. Finally, we design a well-structured denoising network capable of exploring spectrum information while simultaneously sensing context messages. The network has dual paths for modeling high and low-frequency compositions in the raw noisy image. Additionally, context perception modules capture local and global interactions to produce high-quality images. The comprehensive experiments on preclinical photon-counting CT, clinical brain CT, and low-dose CT datasets, demonstrate that our RIP-Net is superior to other unsupervised denoising methods.
摘要：
计算机断层扫描（CT）去噪是医学成像中一项具有挑战性的任务，已引起广泛关注。受监督的网络需要大量嘈杂干净的图像对，在临床环境中始终不可用。现有的用成对噪声图像抑制噪声的自监督算法具有局限性，例如，在训练过程中忽略相似图像对之间的残差，以及对图像频谱信息的学习不足。在这项研究中,我们提出了一个残差图像先验网络（RIP-Net）来充分模拟配对的相似噪声图像之间的残差。我们的方法通过解决现有方法的局限性，为该领域提供了新的见解。我们首先建立了一个数学定理，阐明了基于相似图像的自监督学习和监督学习之间的非等价性。它帮助我们更好地理解自我监督学习的优势和局限性。其次,我们引入了一个新的正则化项来对低频残差图像进行先验建模。这可以提高我们模型的准确性和鲁棒性。最后,我们设计了一个结构良好的去噪网络，能够在探测频谱信息的同时感知上下文消息。该网络具有用于对原始噪声图像中的高频和低频成分进行建模的双路径。此外,上下文感知模块捕获局部和全局交互以生成高质量图像。临床前光子计数CT的综合实验，临床脑部CT,和低剂量CT数据集，证明我们的RIP-Net优于其他无监督去噪方法。
