ArticleFrontiers in oncology2021
A Deep Unsupervised Learning Model for Artifact Correction of Pelvis Cone-Beam CT.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
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Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence-driven preoperative CT 3D planning: a narrative review on improving the accuracy of acetabular cup angle and size in total hip arthroplasty.Frontiers in artificial intelligence · 2026Pooled it
- Deep learning methods for enhancing cone-beam CT image quality toward adaptive radiation therapy: A systematic review.Medical physics · 2022Pooled it
- Prospective multi-institutional study of library-based adaptive radiotherapy for cervical cancer: Evaluation of plan-of-the-day selection and population analysis.Journal of applied clinical medical physics · 2025Trial
- Scatter and beam hardening effect corrections in pelvic region cone beam CT images using a convolutional neural network.Radiological physics and technology · 2025Article
- Synthesizing High b-Value Diffusion-Weighted Imaging of Gastric Cancer Using an Improved Vision Transformer CycleGAN.Bioengineering (Basel, Switzerland) · 2024Article
- Rapid unpaired CBCT-based synthetic CT for CBCT-guided adaptive radiotherapy.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
- Improving performance of medical image alignment through super-resolution.Biomedical engineering letters · 2023Article
- Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency.Physics in medicine and biology · 2022Review
- A Comparison Study Between CNN-Based Deformed Planning CT and CycleGAN-Based Synthetic CT Methods for Improving iCBCT Image Quality.Frontiers in oncology · 2022Article
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10 authors.
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Abstract
purposeIn recent years, cone-beam computed tomography (CBCT) is increasingly used in adaptive radiation therapy (ART). However, compared with planning computed tomography (PCT), CBCT image has much more noise and imaging artifacts. Therefore, it is necessary to improve the image quality and HU accuracy of CBCT. In this study, we developed an unsupervised deep learning network (CycleGAN) model to calibrate CBCT images for the pelvis to extend potential clinical applications in CBCT-guided ART.
methodsTo train CycleGAN to generate synthetic PCT (sPCT), we used CBCT and PCT images as inputs from 49 patients with unpaired data. Additional deformed PCT (dPCT) images attained as CBCT after deformable registration are utilized as the ground truth before evaluation. The trained uncorrected CBCT images are converted into sPCT images, and the obtained sPCT images have the characteristics of PCT images while keeping the anatomical structure of CBCT images unchanged. To demonstrate the effectiveness of the proposed CycleGAN, we use additional nine independent patients for testing.
resultsWe compared the sPCT with dPCT images as the ground truth. The average mean absolute error (MAE) of the whole image on testing data decreased from 49.96 ± 7.21HU to 14.6 ± 2.39HU, the average MAE of fat and muscle ROIs decreased from 60.23 ± 7.3HU to 16.94 ± 7.5HU, and from 53.16 ± 9.1HU to 13.03 ± 2.63HU respectively.
conclusionWe developed an unsupervised learning method to generate high-quality corrected CBCT images (sPCT). Through further evaluation and clinical implementation, it can replace CBCT in ART.
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