ArticleRadiology2015
Quality assurance assessment of diagnostic and radiation therapy-simulation CT image registration for head and neck radiation therapy: anatomic region of interest-based comparison of rigid and deformable algorithms.
Article in Radiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
- Mapping Local Failure Following Bladder Radiotherapy According to Dose.Clinical oncology (Royal College of Radiologists (Great Britain)) · 2022Trial
- A multi-toxicity deep learning approach for normal tissue complication probability modelling in head and neck cancer patients receiving radiotherapy.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2026Article
- Deep learning NTCP model for late dysphagia after radiotherapy for head and neck cancer patients based on 3D dose, CT and segmentations.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2025Article
- Determining Individualized Prognosis of Head and Neck Squamous Cell Carcinoma Patients Treated with Radiotherapy Using a Prediction Model Based on Body Composition Features: Introducing a Probability Calculator.Journal of imaging informatics in medicine · 2025Article
- Evaluation of a comprehensive set of normal tissue complication probability models for patients with head and neck cancer in an international cohort.Oral oncology · 2025Article
- Patterns of failure for recurrent head and neck squamous cell carcinoma treated with salvage surgery and postoperative IMRT reirradiation.Clinical and translational radiation oncology · 2024Article
- Development and implementation of optimized endogenous contrast sequences for delineation in adaptive radiotherapy on a 1.5T MR-linear-accelerator: a prospective R-IDEAL stage 0-2a quantitative/qualitative evaluation ofJournal of medical imaging (Bellingham, Wash.) · 2023Article
- Quality assurance assessment of intra-acquisition diffusion-weighted and T2-weighted magnetic resonance imaging registration and contour propagation for head and neck cancer radiotherapy.Medical physics · 2023Article
- Deformable CT image registration via a dual feasible neural network.Medical physics · 2022Article
- Co-registration of radiotherapy planning and recurrence scans with different imaging modalities in head and neck cancer.Physics and imaging in radiation oncology · 2022Article
- Fully automated rigid image registration versus human registration in postoperative spine stereotactic body radiation therapy: a multicenter non-inferiority study.Journal of radiation research · 2022Article
- Normal Tissue Complication Probability (NTCP) Prediction Model for Osteoradionecrosis of the Mandible in Patients With Head and Neck Cancer After Radiation Therapy: Large-Scale Observational Cohort.International journal of radiation oncology, biology, physics · 2021Observational
- Absence of prostate oedema obviates the need for delay between fiducial marker insertion and radiotherapy simulation.Journal of medical radiation sciences · 2020Article
- Evaluation of a deformable image registration quality assurance tool for head and neck cancer patients.Journal of medical radiation sciences · 2020Article
- Dose-volume correlates of the prevalence of patient-reported trismus in long-term survivorship after oropharyngeal IMRT: A cross-sectional dosimetric analysis.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2020Article
- Precision toxicity correlates of tumor spatial proximity to organs at risk in cancer patients receiving intensity-modulated radiotherapy.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2020Article
- Anatomic change over the course of treatment for non-small cell lung cancer patients and its impact on intensity-modulated radiation therapy and passive-scattering proton therapy deliveries.Radiation oncology (London, England) · 2020Article
- Chronic radiation-associated dysphagia in oropharyngeal cancer survivors: Towards age-adjusted dose constraints for deglutitive muscles.Clinical and translational radiation oncology · 2019Article
- Prospective quantitative quality assurance and deformation estimation of MRI-CT image registration in simulation of head and neck radiotherapy patients.Clinical and translational radiation oncology · 2019Article
- Head and Neck Cancer Adaptive Radiation Therapy (ART): Conceptual Considerations for the Informed Clinician.Seminars in radiation oncology · 2019Review
Corrections and comments
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18 authors.
Funding
Abstract
purposeTo develop a quality assurance (QA) workflow by using a robust, curated, manually segmented anatomic region-of-interest (ROI) library as a benchmark for quantitative assessment of different image registration techniques used for head and neck radiation therapy-simulation computed tomography (CT) with diagnostic CT coregistration. MATERIALS AND
methodsRadiation therapy-simulation CT images and diagnostic CT images in 20 patients with head and neck squamous cell carcinoma treated with curative-intent intensity-modulated radiation therapy between August 2011 and May 2012 were retrospectively retrieved with institutional review board approval. Sixty-eight reference anatomic ROIs with gross tumor and nodal targets were then manually contoured on images from each examination. Diagnostic CT images were registered with simulation CT images rigidly and by using four deformable image registration (DIR) algorithms: atlas based, B-spline, demons, and optical flow. The resultant deformed ROIs were compared with manually contoured reference ROIs by using similarity coefficient metrics (ie, Dice similarity coefficient) and surface distance metrics (ie, 95% maximum Hausdorff distance). The nonparametric Steel test with control was used to compare different DIR algorithms with rigid image registration (RIR) by using the post hoc Wilcoxon signed-rank test for stratified metric comparison.
resultsA total of 2720 anatomic and 50 tumor and nodal ROIs were delineated. All DIR algorithms showed improved performance over RIR for anatomic and target ROI conformance, as shown for most comparison metrics (Steel test, P < .008 after Bonferroni correction). The performance of different algorithms varied substantially with stratification by specific anatomic structures or category and simulation CT section thickness.
conclusionDevelopment of a formal ROI-based QA workflow for registration assessment demonstrated improved performance with DIR techniques over RIR. After QA, DIR implementation should be the standard for head and neck diagnostic CT and simulation CT allineation, especially for target delineation.
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