A dataset-free deep learning method for low-dose CT image reconstruction

Abstract

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 supervised-learning-based methods for LDCT image reconstruction in practice. Aiming at addressing the challenges raised by the collection of a training dataset, this paper proposed an unsupervised DL 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 a deep network with random weights, combined with additional total variational regularization. The experiments show that the proposed method noticeably outperforms existing dataset-free image reconstruction methods on the test data.