A deep unsupervised learning framework for the 4D CBCT artifact correction
- 3 March 2022
- journal article
- research article
- Published by IOP Publishing in Physics in Medicine & Biology
- Vol. 67 (5), 055012
- https://doi.org/10.1088/1361-6560/ac55a5
Abstract
Objective. Four-dimensional cone-beam computed tomography (4D CBCT) has unique advantages in moving target localization, tracking and therapeutic dose accumulation in adaptive radiotherapy. However, the severe fringe artifacts and noise degradation caused by 4D CBCT reconstruction restrict its clinical application. We propose a novel deep unsupervised learning model to generate the high-quality 4D CBCT from the poor-quality 4D CBCT. Approach. The proposed model uses a contrastive loss function to preserve the anatomical structure in the corrected image. To preserve the relationship between the input and output image, we use a multilayer, patch-based method rather than operate on entire images. Furthermore, we draw negatives from within the input 4D CBCT rather than from the rest of the dataset. Main results. The results showed that the streak and motion artifacts were significantly suppressed. The spatial resolution of the pulmonary vessels and microstructure were also improved. To demonstrate the results in the different directions, we make the animation to show the different views of the predicted correction image in the supplementary animation. Significance. The proposed method can be integrated into any 4D CBCT reconstruction method and maybe a practical way to enhance the image quality of the 4D CBCT.Keywords
Funding Information
- National Key R&D Program of China (2018YFE0114800)
- the Guangdong Provincial Administration of Traditional Chinese Medicine (20202159)
This publication has 52 references indexed in Scilit:
- Imaging Doses and Secondary Cancer Risk From Kilovoltage Cone-beam CT in Radiation TherapyHealth Physics, 2013
- Optimizing 4D cone beam computed tomography acquisition by varying the gantry velocity and projection time intervalPhysics in Medicine & Biology, 2013
- Interfractional Positional Variability of Fiducial Markers and Primary Tumors in Locally Advanced Non-Small-Cell Lung Cancer During Audiovisual Biofeedback RadiotherapyEndocrine, 2012
- Comparative study of respiratory motion correction techniques in cone-beam computed tomographyRadiotherapy and Oncology, 2011
- elastix: A Toolbox for Intensity-Based Medical Image RegistrationIEEE Transactions on Medical Imaging, 2009
- High temporal resolution and streak-free four-dimensional cone-beam computed tomographyPhysics in Medicine & Biology, 2008
- Cone-Beam Computed Tomography for On-Line Image Guidance of Lung Stereotactic Radiotherapy: Localization, Verification, and Intrafraction Tumor PositionEndocrine, 2007
- Linac-integrated 4D cone beam CT: first experimental resultsPhysics in Medicine & Biology, 2006
- Motion correction for improved target localization with on-board cone-beam computed tomographyPhysics in Medicine & Biology, 2005
- Assessment of Hepatic Motion Secondary to Respiration for Computer Assisted InterventionsComputer Aided Surgery, 2002