ArticleMedical physics2019
Learning-based CBCT correction using alternating random forest based on auto-context model.
Article in Medical physics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Radiation Oncology-Biology Integration Network: Bridging the Gap between Biological Research and Clinical Practice.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Review
- PE-CycleGAN network based CBCT-sCT generation for nasopharyngeal carsinoma adaptive radiotherapy.Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
- Using a patient-specific diffusion model to generate CBCT-based synthetic CTs for CBCT-guided adaptive radiotherapy.Medical physics · 2025Article
- Characterization of kilovoltage x-ray image guidance system with a novel post-processing algorithm on a new slip ring-mounted radiotherapy system.Journal of applied clinical medical physics · 2024Article
- Synthetic CT generation based on CBCT using improved vision transformer CycleGAN.Scientific reports · 2024Article
- Improving performance of medical image alignment through super-resolution.Biomedical engineering letters · 2023Article
- Improved accuracy of relative electron density and proton stopping power ratio through CycleGAN machine learning.Physics in medicine and biology · 2022Article
- A Comparison Study Between CNN-Based Deformed Planning CT and CycleGAN-Based Synthetic CT Methods for Improving iCBCT Image Quality.Frontiers in oncology · 2022Article
- CBCT-based synthetic CT generation using generative adversarial networks with disentangled representation.Quantitative imaging in medicine and surgery · 2021Article
- Deep learning-based thoracic CBCT correction with histogram matching.Biomedical physics & engineering express · 2021Article
- Knowledge-based radiation treatment planning: A data-driven method survey.Journal of applied clinical medical physics · 2021Review
- Artificial intelligence in tumor subregion analysis based on medical imaging: A review.Journal of applied clinical medical physics · 2021Review
- A review of deep learning based methods for medical image multi-organ segmentation.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2021Review
- Cone beam computed tomography based image guidance and quality assessment of prostate cancer for magnetic resonance imaging-only radiotherapy in the pelvis.Physics and imaging in radiation oncology · 2021Article
- MV CBCT-Based Synthetic CT Generation Using a Deep Learning Method for Rectal Cancer Adaptive Radiotherapy.Frontiers in oncology · 2021Article
- Imaging Study of Pseudo-CT Synthesized From Cone-Beam CT Based on 3D CycleGAN in Radiotherapy.Frontiers in oncology · 2021Article
- Deep learning in medical image registration: a review.Physics in medicine and biology · 2020Review
- CT-based multi-organ segmentation using a 3D self-attention U-net network for pancreatic radiotherapy.Medical physics · 2020Article
- Machine learning in quantitative PET: A review of attenuation correction and low-count image reconstruction methods.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2020Review
- CBCT-based synthetic CT generation using deep-attention cycleGAN for pancreatic adaptive radiotherapy.Medical physics · 2020Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
purposeQuantitative Cone Beam CT (CBCT) imaging is increasing in demand for precise image-guided radiotherapy because it provides a foundation for advanced image-guided techniques, including accurate treatment setup, online tumor delineation, and patient dose calculation. However, CBCT is currently limited only to patient setup in the clinic because of the severe issues in its image quality. In this study, we develop a learning-based approach to improve CBCT's image quality for extended clinical applications. MATERIALS AND
methodsAn auto-context model is integrated into a machine learning framework to iteratively generate corrected CBCT (CCBCT) with high-image quality. The first step is data preprocessing for the built training dataset, in which uninformative image regions are removed, noise is reduced, and CT and CBCT images are aligned. After a CBCT image is divided into a set of patches, the most informative and salient anatomical features are extracted to train random forests. Within each patch, alternating RF is applied to create a CCBCT patch as the output. Moreover, an iterative refinement strategy is exercised to enhance the image quality of CCBCT. Then, all the CCBCT patches are integrated to reconstruct final CCBCT images.
resultsThe learning-based CBCT correction algorithm was evaluated using the leave-one-out cross-validation method applied on a cohort of 12 patients' brain data and 14 patients' pelvis data. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR), normalized cross-correlation (NCC) indexes, and spatial nonuniformity (SNU) in the selected regions of interest (ROIs) were used to quantify the proposed algorithm's correction accuracy and generat the following results: mean MAE = 12.81 ± 2.04 and 19.94 ± 5.44 HU, mean PSNR = 40.22 ± 3.70 and 31.31 ± 2.85 dB, mean NCC = 0.98 ± 0.02 and 0.95 ± 0.01, and SNU = 2.07 ± 3.36% and 2.07 ± 3.36% for brain and pelvis data.
conclusionPreliminary results demonstrated that the novel learning-based correction method can significantly improve CBCT image quality. Hence, the proposed algorithm is of great potential in improving CBCT's image quality to support its clinical utility in CBCT-guided adaptive radiotherapy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.