ArticlePhysics and imaging in radiation oncology2021
Synthetic computed tomography data allows for accurate absorbed dose calculations in a magnetic resonance imaging only workflow for head and neck radiotherapy.
Article in Physics and imaging in radiation oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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34 citing papers in PubMed.
- Application of synthetic computed tomography based on a deep learning model in pelvic radiotherapy planning: A feasibility study.Journal of applied clinical medical physics · 2026Article
- How bone mis-segmentation affects MRI-based sCT dosimetry in head and neck radiotherapy: A CT-referenced study.Journal of applied clinical medical physics · 2026Article
- Image-based evaluation of a commercial AI synthetic CT generator for brain and prostate MR-only radiotherapy.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
- Phantom benchmarking for quality assurance of synthetic computed tomography in cranial magnetic resonance-only radiotherapy.Physics and imaging in radiation oncology · 2026Article
- Improved synthetic CT generation using surface scan information integrated into a deformable registration algorithm for limited Field of View data.Physics and imaging in radiation oncology · 2026Article
- Automated MR-only radiotherapy outperforms CT-based radiotherapy and decreases hands-on time for head-and-neck cancer treatment.Clinical and translational radiation oncology · 2026Article
- Development of an MR-only radiotherapy treatment planning workflow using a commercial synthetic CT generator for brain and head & neck tumor patients.Zeitschrift fur medizinische Physik · 2026Article
- Dosimetric evaluation of magnetic resonance imaging based synthetic computed tomography for head and neck photon and proton therapy.Journal of applied clinical medical physics · 2025Article
- Clinical implementation of deep learning-based synthetic CT for MRI-only volumetric modulated arc therapy in head and neck and pelvic cancer patients.Radiation oncology (London, England) · 2025Article
- An approach for cancer outcomes modelling using a comprehensive synthetic dataset.Physical and engineering sciences in medicine · 2025Article
- Deep Learning-Based Heterogeneity Correction of the Homogeneous Dose Distribution for Single Brain Tumors in Gamma Knife Radiosurgery.Advances in radiation oncology · 2025Article
- Quality evaluation of synthetic CT using a gamma analysis for prostate cancer MR-only radiotherapy.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology · 2025Article
- Advancements in synthetic CT generation from MRI: A review of techniques, and trends in radiation therapy planning.Journal of applied clinical medical physics · 2024Review
- Linac- and CyberKnife-based MRI-only treatment planning of prostate SBRT using an optimized synthetic CT calibration curve.Journal of applied clinical medical physics · 2024Article
- Incidental findings and safety events from magnetic resonance imaging simulation for head and neck radiation treatment planning: A single institution experience.Technical innovations & patient support in radiation oncology · 2024Article
- Evaluating the Hounsfield unit assignment and dose differences between CT-based standard and deep learning-based synthetic CT images for MRI-only radiation therapy of the head and neck.Journal of applied clinical medical physics · 2024Article
- The Use of MR-Guided Radiation Therapy for Head and Neck Cancer and Recommended Reporting Guidance.Seminars in radiation oncology · 2024Review
- Evaluation of a deep learning magnetic resonance imaging reconstruction method for synthetic computed tomography generation in prostate radiotherapy.Physics and imaging in radiation oncology · 2024Article
- Review and recommendations on deformable image registration uncertainties for radiotherapy applications.Physics in medicine and biology · 2023Review
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7 authors.
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Abstract
background and purposeFew studies on magnetic resonance imaging (MRI) only head and neck radiation treatment planning exist, and none using a generally available software. The aim of this study was to evaluate the accuracy of absorbed dose for head and neck synthetic computed tomography data (sCT) generated by a commercial convolutional neural network-based algorithm. MATERIALS AND
methodsFor 44 head and neck cancer patients, sCT were generated and the geometry was validated against computed tomography data (CT). The clinical CT based treatment plan was transferred to the sCT and recalculated without re-optimization, and differences in relative absorbed dose were determined for dose-volume-histogram (DVH) parameters and the 3D volume.
resultsFor overall body, the results of the geometric validation were (Mean ± 1sd): Mean error -5 ± 10 HU, mean absolute error 67 ± 14 HU, Dice similarity coefficient 0.98 ± 0.05, and Hausdorff distance difference 4.2 ± 1.7 mm. Water equivalent depth difference for region Th1-C7, mid mandible and mid nose were -0.3 ± 3.4, 1.1 ± 2.0 and 0.7 ± 3.8 mm respectively. The maximum mean deviation in absorbed dose for all DVH parameters was 0.30% (0.12 Gy). The absorbed doses were considered equivalent (p-value < 0.001) and the mean 3D gamma passing rate was 99.4 (range: 95.7-99.9%).
conclusionsThe convolutional neural network-based algorithm generates sCT which allows for accurate absorbed dose calculations for MRI-only head and neck radiation treatment planning. The sCT allows for statistically equivalent absorbed dose calculations compared to CT based radiotherapy.
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