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

• URL: http://arxiv.org/abs/2205.00463v1
• Date: Sun, 1 May 2022 13:05:04 GMT
• Title: Dataset-free Deep learning Method for Low-Dose CT Image Reconstruction
• Authors: Qiaoqiao Ding, Hui Ji, Yuhui Quan, Xiaoqun Zhang
• Abstract summary: This paper proposes a unsupervised deep learning method for LDCT image reconstruction. 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. Experiments show that the proposed method noticeably outperforms existing dataset-free image reconstruction methods on the test data.
• Score: 33.193423488300255
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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