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

A Dataset-free Deep learning Method for Low-Dose CT Image Reconstruction