论文标题
低剂量CT DeNoising的概率自学习框架
Probabilistic self-learning framework for Low-dose CT Denoising
论文作者
论文摘要
尽管X射线计算机断层扫描（CT）在诊断医学领域中具有不可或缺的作用，但考虑到可能引起遗传和癌性疾病，相关的电离辐射仍然是一个主要问题。减少暴露量可以减少剂量，从而减少与辐射相关的风险，但也会引起更高的量子噪声。有监督的深度学习可用于训练神经网络，以降低低剂量CT（LDCT）。但是，其成功需要大量的像素配对LDCT和正常剂量CT（NDCT）图像，这些图像在实际实践中很少可用。为了缓解这个问题，在本文中，设计了基于偏移不变的属性的神经网络，以了解固有的像素相关性以及仅通过使用LDCT图像来学习噪声分布，从而将其塑造成我们的概率自学框架。实验结果表明，该提出的方法的表现优于竞争对手，产生了增强的LDCT图像，该图像样式与常规NDCT相似，在临床实践中高度优先。
Despite the indispensable role of X-ray computed tomography (CT) in diagnostic medicine field, the associated ionizing radiation is still a major concern considering that it may cause genetic and cancerous diseases. Decreasing the exposure can reduce the dose and hence the radiation-related risk, but will also induce higher quantum noise. Supervised deep learning can be used to train a neural network to denoise the low-dose CT (LDCT). However, its success requires massive pixel-wise paired LDCT and normal-dose CT (NDCT) images, which are rarely available in real practice. To alleviate this problem, in this paper, a shift-invariant property based neural network was devised to learn the inherent pixel correlations and also the noise distribution by only using the LDCT images, shaping into our probabilistic self-learning framework. Experimental results demonstrated that the proposed method outperformed the competitors, producing an enhanced LDCT image that has similar image style as the routine NDCT which is highly-preferable in clinic practice.
