ArticleFrontiers in oncology2022
Comparison of Synthetic Computed Tomography Generation Methods, Incorporating Male and Female Anatomical Differences, for Magnetic Resonance Imaging-Only Definitive Pelvic Radiotherapy.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 17 citations in OpenAlex.
- Optimizing the delivery of radiotherapy with artificial intelligence.Nature reviews. Clinical oncology · 2026Review
- Image-based evaluation of a commercial AI synthetic CT generator for brain and prostate MR-only radiotherapy.Journal of applied clinical medical physics · 2026Article
- Synthetic CT imaging for pelvic and abdominal MR-only radiotherapy: Clinical validation on a 1.5T MR-Linac.Journal of applied clinical medical physics · 2026Article
- 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
- Sex estimation with convolutional neural networks using the patella magnetic resonance image slices.Forensic science, medicine, and pathology · 2025Article
- Evaluation and comparison of synthetic computed tomography algorithms with 3T MRI for prostate radiotherapy: AI-based versus bulk density method.Journal of applied clinical medical physics · 2025Article
- Synthetic CT generation from CBCT and MRI using StarGAN in the Pelvic Region.Radiation oncology (London, England) · 2025Article
- Synthetic CT generation from CBCT using deep learning for adaptive radiotherapy in prostate cancer.Frontiers in radiology · 2025Article
- Synthetic Computed Tomography generation using deep-learning for female pelvic radiotherapy planning.Physics and imaging in 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
- 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
- Machine Learning for Medical Image Translation: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- Cone beam computed tomography image guidance within a magnetic resonance imaging-only planning workflow.Physics and imaging in radiation oncology · 2023Article
- Synthetic computed tomography for low-field magnetic resonance-only radiotherapy in head-and-neck cancer using residual vision transformers.Physics and imaging in radiation oncology · 2023Article
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6 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
purposeThere are several means of synthetic computed tomography (sCT) generation for magnetic resonance imaging (MRI)-only planning; however, much of the research omits large pelvic treatment regions and female anatomical specific methods. This research aimed to apply four of the most popular methods of sCT creation to facilitate MRI-only radiotherapy treatment planning for male and female anorectal and gynecological neoplasms. sCT methods were validated against conventional computed tomography (CT), with regard to Hounsfield unit (HU) estimation and plan dosimetry. METHODS AND MATERIALS: Paired MRI and CT scans of 40 patients were used for sCT generation and validation. Bulk density assignment, tissue class density assignment, hybrid atlas, and deep learning sCT generation methods were applied to all 40 patients. Dosimetric accuracy was assessed by dose difference at reference point, dose volume histogram (DVH) parameters, and 3D gamma dose comparison. HU estimation was assessed by mean error and mean absolute error in HU value between each sCT and CT.
resultsThe median percentage dose difference between the CT and sCT was <1.0% for all sCT methods. The deep learning method resulted in the lowest median percentage dose difference to CT at -0.03% (IQR 0.13, -0.31) and bulk density assignment resulted in the greatest difference at -0.73% (IQR -0.10, -1.01). The mean 3D gamma dose agreement at 3%/2 mm among all sCT methods was 99.8%. The highest agreement at 1%/1 mm was 97.3% for the deep learning method and the lowest was 93.6% for the bulk density method. Deep learning and hybrid atlas techniques gave the lowest difference to CT in mean error and mean absolute error in HU estimation.
conclusionsAll methods of sCT generation used in this study resulted in similarly high dosimetric agreement for MRI-only planning of male and female cancer pelvic regions. The choice of the sCT generation technique can be guided by department resources available and image guidance considerations, with minimal impact on dosimetric accuracy.
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