A dataset-free deep learning method for low-dose CT image reconstruction. (1st October 2022)

• Record Type: Journal Article
• Authors: Ding, Qiaoqiao; Ji, Hui; Quan, Yuhui; Zhang, Xiaoqun
• Is Part Of: Inverse problems. Volume 38:Number 10(2022)
• Publication Date: 2022-10-01
• Subjects: x-ray CT -- low dose CT -- deep neural network -- unsupervised learning; Inverse problems (Differential equations) -- Periodicals; 515.357
• DOI: 10.1088/1361-6420/ac8ac6
• Languages: English
• ISSNs: 0266-5611

Abstract

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.