ReviewMedical physics2024
A review on 4D cone-beam CT (4D-CBCT) in radiation therapy: Technical advances and clinical applications.
Review in Medical physics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Quantitative evaluation of respiratory motion-induced blurring in expiratory-phase CBCT for pancreatic SBRT.Journal of applied clinical medical physics · 2026Article
- Synthesizing cone-beam projections in radiotherapy using deep learning network for patients with head and neck cancer.Quantitative imaging in medicine and surgery · 2026Article
- Review
- Refining imaging parameters for dual-energy cone-beam computed tomography in image-guided radiation therapy.Journal of applied clinical medical physics · 2026Article
- Computed tomography for four-dimensional dose calculation: Effect of detector array length.Journal of applied clinical medical physics · 2026Article
- Quantifying the impact of tumor size and motion on 4DCT-4DCBCT image registration accuracy using machine learning and statistical analysis.Journal of applied clinical medical physics · 2026Article
- Case Report: Dose-escalated volumetric modulated arc therapy achieving bowel and renal protection for large retroperitoneal inflammatory myofibroblastic tumor invading ureter and inferior vena cava.Frontiers in oncology · 2026Article
- A bias field correction workflow based on generative adversarial network for abdominal cancers treated with 0.35T MR-LINAC.Journal of applied clinical medical physics · 2026Article
- PhysMorph: A biomechanical and image-guided deep learning framework for real-time multi-modal liver image registration.Physics and imaging in radiation oncology · 2026Article
- Dynamic cone beam CT reconstruction via spatiotemporal Gaussian neural representation.Medical physics · 2025Article
- Clinical comparison of adaptive 4DCBCT scanning protocols for lung tumor motion assessment.Journal of applied clinical medical physics · 2025Article
- A conditional point cloud diffusion model for deformable liver motion tracking via a single arbitrarily-angled x-ray projection.Physics in medicine and biology · 2025Article
- Artificial intelligence in four-dimensional imaging for motion management in radiation therapy.Artificial intelligence review · 2025Article
- Stereotactic body radiation therapy for early-stage non-small cell lung cancer: a single-institutional retrospective analysis of outcomes and prognostic factors.Frontiers in oncology · 2025Article
- Comparative Evaluation of Conventional and Deep Learning Methods for Respiratory Signal Extraction From Clinical 3D CBCT Projections.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Cone-beam CT (CBCT) is the most commonly used onboard imaging technique for target localization in radiation therapy. Conventional 3D CBCT acquires x-ray cone-beam projections at multiple angles around the patient to reconstruct 3D images of the patient in the treatment room. However, despite its wide usage, 3D CBCT is limited in imaging disease sites affected by respiratory motions or other dynamic changes within the body, as it lacks time-resolved information. To overcome this limitation, 4D-CBCT was developed to incorporate a time dimension in the imaging to account for the patient's motion during the acquisitions. For example, respiration-correlated 4D-CBCT divides the breathing cycles into different phase bins and reconstructs 3D images for each phase bin, ultimately generating a complete set of 4D images. 4D-CBCT is valuable for localizing tumors in the thoracic and abdominal regions where the localization accuracy is affected by respiratory motions. This is especially important for hypofractionated stereotactic body radiation therapy (SBRT), which delivers much higher fractional doses in fewer fractions than conventional fractionated treatments. Nonetheless, 4D-CBCT does face certain limitations, including long scanning times, high imaging doses, and compromised image quality due to the necessity of acquiring sufficient x-ray projections for each respiratory phase. In order to address these challenges, numerous methods have been developed to achieve fast, low-dose, and high-quality 4D-CBCT. This paper aims to review the technical developments surrounding 4D-CBCT comprehensively. It will explore conventional algorithms and recent deep learning-based approaches, delving into their capabilities and limitations. Additionally, the paper will discuss the potential clinical applications of 4D-CBCT and outline a future roadmap, highlighting areas for further research and development. Through this exploration, the readers will better understand 4D-CBCT's capabilities and potential to enhance radiation therapy.
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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.