ReviewPhysics in medicine and biology2023
Review and recommendations on deformable image registration uncertainties for radiotherapy applications.
Review in Physics in medicine and biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
46 citing papers in PubMed.
- Inverse-consistent deformable image registration to propagate biomechanical constraints for online adaptive radiotherapy.Medical physics · 2026Article
- Beyond the LUMIR challenge: The pathway to foundational registration models.Medical image analysis · 2026Article
- Cross-fraction prior learning for scalable organ-at-risk segmentation in abdominal MR-guided radiotherapy.Medical physics · 2026Article
- Towards automated dose-guided patient positioning at clinical timescale in head-and-neck cancer proton radiotherapy.Medical physics · 2026Article
- One-for-All Adaptive Radiotherapy Planning Agent: A Foundation Framework for Daily CBCT-guided Radiotherapy.ArXiv · 2026Article
- Synthetic CT generation from cone-beam CT using deep learning for head-and-neck adaptive radiotherapy.Physics and imaging in radiation oncology · 2026Article
- Extended reality simulator for dynamic visualization and evaluation of stereotactic arrhythmia radioablation (STAR) treatment plans within RAVENTA trial.European heart journal. Digital health · 2026Article
- Impact of dose calculation algorithm on magnetic resonance imaging-only radiotherapy for prostate and glioma.Physics and imaging in radiation oncology · 2026Article
- [A reweighted graph matching-based algorithm for automatic extraction of corresponding landmarks in medical images].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- Review
- Validation of Synthetic Megavoltage Computed Tomography (MVCT) for Dose Calculation in Radiotherapy Treatment Planning.Cancers · 2026Article
- Evaluation of classical and deep-learning deformable registration for magnetic resonance to four-dimensional computed tomography contour mapping in liver stereotactic body radiotherapy.Physics and imaging in radiation oncology · 2026Article
- A Dosimetric Comparison of the Accumulated Dose in Prostate SBRT for Non-Adaptive and Adaptive External Beam Radiotherapy.Cancers · 2026Article
- Robust organ mapped dose: using multiple image registrations to identify deformation uncertainty in radiation dose mapping.Physics in medicine and biology · 2026Article
- Reduction of organ of interest dose in proton therapy for oesophageal cancer through optimized setup robustness settings and online adaptation.Physics and imaging in radiation oncology · 2026Article
- A dedicated institutional clinical workflow and peer-review meeting for reirradiation.Technical innovations & patient support in radiation oncology · 2026Article
- Landmark-guided deformable image registration for accurate cumulative dose evaluation in lung reirradiation.Physics and imaging in radiation oncology · 2026Article
- Comparative evaluation of dose accumulation strategies in the clinical reirradiation setting.Physics and imaging in radiation oncology · 2026Article
- Reducing low-dose exposure in helical TomoTherapy for locally advanced left-sided breast cancer with a deformable image registration-based dose-mimicking workflow.Journal of applied clinical medical physics · 2026Article
- Accuracy of reirradiation dose constraints for the mandible and carotids.Physics and imaging in radiation oncology · 2026Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Deformable image registration (DIR) is a versatile tool used in many applications in radiotherapy (RT). DIR algorithms have been implemented in many commercial treatment planning systems providing accessible and easy-to-use solutions. However, the geometric uncertainty of DIR can be large and difficult to quantify, resulting in barriers to clinical practice. Currently, there is no agreement in the RT community on how to quantify these uncertainties and determine thresholds that distinguish a good DIR result from a poor one. This review summarises the current literature on sources of DIR uncertainties and their impact on RT applications. Recommendations are provided on how to handle these uncertainties for patient-specific use, commissioning, and research. Recommendations are also provided for developers and vendors to help users to understand DIR uncertainties and make the application of DIR in RT safer and more reliable.
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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 OpenQuestion graph.