Fast four‐dimensional cone‐beam computed tomography reconstruction using deformable convolutional networks. Issue 10 (22nd June 2022)

• Record Type: Journal Article
• Authors: Jiang, Zhuoran; Chang, Yushi; Zhang, Zeyu; Yin, Fang‐Fang; Ren, Lei
• Is Part Of: Medical physics. Volume 49:Issue 10(2022)
• Page Start: 6461
• Page End: 6476
• Publication Date: 2022-06-22
• Subjects: 4D‐CBCT reconstruction -- deep learning -- deformable convolutional networks -- fast acquisition -- feature compensation; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.15806
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract: Background: Although four‐dimensional cone‐beam computed tomography (4D‐CBCT) is valuable to provide onboard image guidance for radiotherapy of moving targets, it requires a long acquisition time to achieve sufficient image quality for target localization. To improve the utility, it is highly desirable to reduce the 4D‐CBCT scanning time while maintaining high‐quality images. Current motion‐compensated methods are limited by slow speed and compensation errors due to the severe intraphase undersampling. Purpose: In this work, we aim to propose an alternative feature‐compensated method to realize the fast 4D‐CBCT with high‐quality images. Methods: We proposed a feature‐compensated deformable convolutional network (FeaCo‐DCN) to perform interphase compensation in the latent feature space, which has not been explored by previous studies. In FeaCo‐DCN, encoding networks extract features from each phase, and then, features of other phases are deformed to those of the target phase via deformable convolutional networks. Finally, a decoding network combines and decodes features from all phases to yield high‐quality images of the target phase. The proposed FeaCo‐DCN was evaluated using lung cancer patient data. Results: (1) FeaCo‐DCN generated high‐quality images with accurate and clear structures for a fast 4D‐CBCT scan; (2) 4D‐CBCT images reconstructed by FeaCo‐DCN achieved 3D tumor localization accuracy within 2.5 mm; (3) image reconstruction is nearly real time; and (4) FeaCo‐DCN achieved superior performance by all metrics compared to the top‐ranked techniques in the AAPM SPARE Challenge. Conclusion: The proposed FeaCo‐DCN is effective and efficient in reconstructing 4D‐CBCT while reducing about 90% of the scanning time, which can be highly valuable for moving target localization in image‐guided radiotherapy.