  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 (DL) 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.
