A Dataset-free Deep learning Method for Low-Dose CT Image Reconstruction
This addresses the challenge of collecting paired training data in clinical settings for low-dose CT imaging, offering a practical solution for reducing radiation exposure, though it is incremental as it builds on existing dataset-free approaches.
The paper tackles the problem of low-dose CT image reconstruction without requiring external training data by proposing an unsupervised deep learning method that combines a re-parametrization technique for Bayesian inference with total variational regularization. The result is that the method noticeably outperforms existing dataset-free methods on test data.
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.