ArticlePhysics and imaging in radiation oncology2023
Accuracy of deformable image registration-based intra-fraction motion management in Magnetic Resonance-guided radiotherapy.
Article in Physics and imaging in radiation oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 11 citations in OpenAlex.
- Real-time tumor tracking for magnetic resonance-guided radiotherapy using label-efficient foundation model adaptation.Physics and imaging in radiation oncology · 2026Article
- Online adaptive radiotherapy: International strategies for AI-enabled workflow efficiency and radiation therapist-led delivery for sustainable practice.Technical innovations & patient support in radiation oncology · 2026Article
- Automated annotation error detection and correction for manual two-dimensional cinematic magnetic resonance imaging segmentations using segment anything 2.Physics and imaging in radiation oncology · 2026Article
- Real-time target localization on 1.5 T magnetic resonance imaging linac orthogonal cine images using transfer learning.Physics and imaging in radiation oncology · 2025Article
- Treatment planning evaluation and experimental validation of the magnetic resonance-based intrafraction drift correction.Physics and imaging in radiation oncology · 2024Article
- Evaluation of clinical parallel workflow in online adaptive MR-guided Radiotherapy: A detailed assessment of treatment session times.Technical innovations & patient support in radiation oncology · 2024Article
- Current and future developments of synthetic computed tomography generation for radiotherapy.Physics and imaging in radiation oncology · 2023Article
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Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Purpose: Intra-fraction motion management is key in Stereotactic Ablative Radiotherapy (SABR) gated delivery. This study assessed the accuracy of automatic tumor segmentation in the delivery of MR-guided radiotherapy (MRgRT) by comparing it to manual delineations performed by experienced observers. Materials and Methods: Twenty patients previously treated with MR-guided SABR for thoracic and abdominal tumors were included. Five observers with at least two years of experience in MRgRT manually delineated the gross tumor volume (GTV) for 20 patients on 240 frames of a cine MRI on a sagittal plane. Deformable Image Registration (DIR) based GTV contours were propagated using four different algorithms from a reference frame to subsequent frames.Geometrical analysis based on the Dice Similarity Coefficient (DSC), centroid distance and Hausdorff Distance (HDD) were performed to assess the inter-observer variability and the accuracy of automatic segmentation. A Confidence Value (CV) metric for the reliability of the tumor auto-contouring was also calculated. Results: Inter-observer delineation variability resulted in mean DSC of 0.89, HDD of 5.8 mm and centroid distance of 1.7 mm. Tumor auto-contouring by the four DIR algorithms resulted in an excellent agreement with the manual delineations by the experienced observers. Mean DSC for each algorithm across all patients was greater than 0.90, whereas the HDD and centroid distances were below 4.0 mm and 1.5 mm, respectively. The CV showed a strong correlation with the DSC. Conclusions: DIR-based auto-contouring in MRgRT exhibited a high level of agreement with the manual contouring performed by experts, allowing accurate gated delivery.
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