Low-dose CT (LDCT) imaging attracted a considerable interest for the reduction of the object's exposure to X-ray radiation. In recent years, supervised deep learning has been extensively studied for LDCT image reconstruction, which trains a network over a dataset containing many pairs of normal-dose and low-dose images. However, the challenge on collecting many such pairs in the clinical setup limits the application of such supervised-learning-based methods for LDCT image reconstruction in practice. Aiming at addressing the challenges raised by the collection of training dataset, this paper proposed a unsupervised deep learning method for LDCT image reconstruction, which does not require any external training data. The proposed method is built on a re-parametrization technique for Bayesian inference via deep network with random weights, combined with additional total variational (TV) regularization. The experiments show that the proposed method noticeably outperforms existing dataset-free image reconstruction methods on the test data.

翻译：低剂量CT(LDCT)成像在减少物体受X射线辐射照射方面引起了相当大的兴趣。近年来,在LDCT图像重建方面,广泛研究了受监督的深层次学习,通过包含许多正常剂量和低剂量图像的数据集对网络进行了培训,但是,在临床设备中收集许多这类配对的挑战限制了在实践中应用这种以监督学习为基础的方法来重建LDCT图像。为了应对培训数据集收集工作带来的挑战,本文件建议为LDCT图像重建采用一种不受监督的深层次学习方法,这不需要任何外部培训数据。拟议的方法建立在通过随机重量的深层网络对Bayesian的推断进行再平衡技术的基础上,加上额外的全变式(TV)规范。实验表明,拟议的方法明显地超过了测试数据上现有的无数据数据集图像重建方法。