ArticleFrontiers in oncology2022
A novel approach for eliminating metal artifacts based on MVCBCT and CycleGAN.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Deep learning-based metal artifact reduction in CT for head and neck radiotherapy.Physics and imaging in radiation oncology · 2026Review
- Dosimetric evaluation of synthetic kilo-voltage CT images generated from megavoltage CT for head and neck tomotherapy using a conditional GAN network.Physical and engineering sciences in medicine · 2025Article
- Dual-encoder architecture for metal artifact reduction for kV-cone-beam CT images in head and neck cancer radiotherapy.Scientific reports · 2024Article
- Synthetic Megavoltage Cone Beam Computed Tomography Image Generation for Improved Contouring Accuracy of Cardiac Pacemakers.Journal of imaging · 2023Article
- Effect of metal implants and metal artifacts on back-projected two-dimensional entrance fluence determined by EPID dosimetry.Journal of applied clinical medical physics · 2023Article
- Improving synthetic CT accuracy by combining the benefits of multiple normalized preprocesses.Journal of applied clinical medical physics · 2023Article
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Abstract
Purpose: To develop a metal artifact reduction (MAR) algorithm and eliminate the adverse effects of metal artifacts on imaging diagnosis and radiotherapy dose calculations. Methods: Cycle-consistent adversarial network (CycleGAN) was used to generate synthetic CT (sCT) images from megavoltage cone beam CT (MVCBCT) images. In this study, there were 140 head cases with paired CT and MVCBCT images, from which 97 metal-free cases were used for training. Based on the trained model, metal-free sCT (sCT_MF) images and metal-containing sCT (sCT_M) images were generated from the MVCBCT images of 29 metal-free cases and 14 metal cases, respectively. Then, the sCT_MF and sCT_M images were quantitatively evaluated for imaging and dosimetry accuracy. Results: The structural similarity (SSIM) index of the sCT_MF and metal-free CT (CT_MF) images were 0.9484, and the peak signal-to-noise ratio (PSNR) was 31.4 dB. Compared with the CT images, the sCT_MF images had similar relative electron density (RED) and dose distribution, and their gamma pass rate (1 mm/1%) reached 97.99% ± 1.14%. The sCT_M images had high tissue resolution with no metal artifacts, and the RED distribution accuracy in the range of 1.003 to 1.056 was improved significantly. The RED and dose corrections were most significant for the planning target volume (PTV), mandible and oral cavity. The maximum correction of Dmean and D50 for the oral cavity reached 90 cGy. Conclusions: Accurate sCT_M images were generated from MVCBCT images based on CycleGAN, which eliminated the metal artifacts in clinical images completely and corrected the RED and dose distributions accurately for clinical application.
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