2D/3D Non-Rigid Image Registration via Two Orthogonal X-ray Projection Images for Lung Tumor Tracking

Autor: Guoya Dong, Jingjing Dai, Na Li, Chulong Zhang, Wenfeng He, Lin Liu, Yinping Chan, Yunhui Li, Yaoqin Xie, Xiaokun Liang
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Bioengineering, Vol 10, Iss 2, p 144 (2023)
Druh dokumentu: article
ISSN: 10020144
2306-5354
DOI: 10.3390/bioengineering10020144
Popis: Two-dimensional (2D)/three-dimensional (3D) registration is critical in clinical applications. However, existing methods suffer from long alignment times and high doses. In this paper, a non-rigid 2D/3D registration method based on deep learning with orthogonal angle projections is proposed. The application can quickly achieve alignment using only two orthogonal angle projections. We tested the method with lungs (with and without tumors) and phantom data. The results show that the Dice and normalized cross-correlations are greater than 0.97 and 0.92, respectively, and the registration time is less than 1.2 seconds. In addition, the proposed model showed the ability to track lung tumors, highlighting the clinical potential of the proposed method.
Databáze: Directory of Open Access Journals
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