Advances in low-dose cone-beam computed tomography image reconstruction methods based on deep learning.
10.7507/1001-5515.202409021
- Author:
Jiangyuan SHI
1
;
Ying SONG
1
;
Guangjun LI
1
;
Sen BAI
1
Author Information
1. Department of Radiotherapy Physics & Technology, West China Hospital, Sichuan University, Chengdu 610041, P. R. China.
- Publication Type:English Abstract
- Keywords:
Deep learning;
Image reconstruction;
Low-dose cone-beam computed tomography
- MeSH:
Cone-Beam Computed Tomography/methods*;
Deep Learning;
Humans;
Algorithms;
Image Processing, Computer-Assisted/methods*;
Radiation Dosage;
Artifacts
- From:
Journal of Biomedical Engineering
2025;42(3):635-642
- CountryChina
- Language:Chinese
-
Abstract:
Cone-beam computed tomography (CBCT) is widely used in dentistry, surgery, radiotherapy and other medical fields. However, repeated CBCT scans expose patients to additional radiation doses, increasing the risk of secondary malignant tumors. Low-dose CBCT image reconstruction technology, which employs advanced algorithms to reduce radiation dose while enhancing image quality, has emerged as a focal point of recent research. This review systematically examined deep learning-based methods for low-dose CBCT reconstruction. It compared different network architectures in terms of noise reduction, artifact removal, detail preservation, and computational efficiency, covering three approaches: image-domain, projection-domain, and dual-domain techniques. The review also explored how emerging technologies like multimodal fusion and self-supervised learning could enhance these methods. By summarizing the strengths and weaknesses of current approaches, this work provides insights to optimize low-dose CBCT algorithms and support their clinical adoption.
