[Advances in low-dose cone-beam computed tomography image reconstruction methods based on deep learning]

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.

Keywords: Deep learning; Image reconstruction; Low-dose cone-beam computed tomography.

图 1

Overview of the current research on low-dose CBCT image reconstruction 低剂量CBCT图像重建研究现状

图 2

The workflow of image-projection domain reconstruction 图像-投影域协同方法的重建流程

图 3

Reconstruction performances for images with metal artifacts 含金属伪影的图像重建对比图^[42]