ArticleJournal of medical radiation sciences2020
Evaluation of a deformable image registration quality assurance tool for head and neck cancer patients.
Article in Journal of medical radiation sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Early Salivary Gland Shrinkage Is Associated With an Increased Risk of Acute Xerostomia in Head and Neck Cancer Radiation Therapy.Advances in radiation oncology · 2026Article
- Improving patient specific quality assurance for image registration: clinical use case of target contouring for PET deformable image registration.Physical and engineering sciences in medicine · 2025Article
- A fully automated machine-learning-based workflow for radiation treatment planning in prostate cancer.Clinical and translational radiation oncology · 2025Article
- Deformable dose accumulation is required for adaptive radiotherapy practice.Journal of applied clinical medical physics · 2024Article
- Evaluation and mitigation of deformable image registration uncertainties for MRI-guided adaptive radiotherapy.Journal of applied clinical medical physics · 2024Article
- Deformable CT image registration via a dual feasible neural network.Medical physics · 2022Article
- Deep Diffusion MRI Registration (DDMReg): A Deep Learning Method for Diffusion MRI Registration.IEEE transactions on medical imaging · 2022Article
- Deformable image registration in radiation therapy.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
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Authors and funding
5 authors.
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Abstract
introductionA challenge in implementing deformable image registration (DIR) in radiation therapy planning is effectively communicating registration accuracy to the radiation oncologist. This study aimed to evaluate the MIM® quality assurance (QA) tool for rating DIR accuracy.
methodsRetrospective DIR was performed on CT images for 35 head and neck cancer patients. The QA tool was used to rate DIR accuracy as good, fair or bad. Thirty registered patient images were assessed independently by three RTs and a further five patients assessed by five RTs. Ratings were evaluated by comparison of Hausdorff Distance (HD), Mean Distance to Agreement (MDA), Dice Similarity Coefficients (DSC) and Jacobian determinants for parotid and mandible subregions on the two CTs post-DIR. Inter-operator reliability was assessed using Krippendorff's alpha coefficient (KALPA). Rating time and volume measures for each rating were also calculated.
resultsQuantitative metrics calculated for most anatomical subregions reflected the expected trend by registration accuracy, with good obtaining the most ideal values on average (HD = 7.50 ± 3.18, MDA = 0.64 ± 0.47, DSC = 0.90 ± 0.07, Jacobian = 0.95 ± 0.06). Highest inter-operator reliability was observed for good ratings and within the parotids (KALPA 0.66-0.93), whilst ratings varied the most in regions of dental artefact. Overall, average rating time was 33 minutes and the least commonly applied rating by volume was fair.
conclusionResults from qualitative and quantitative data, operator rating differences and rating time suggest highlighting only bad regions of DIR accuracy and implementing clinical guidelines and RT training for consistent and efficient use of the QA tool.
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