ArticleJournal of applied clinical medical physics2019
Dosimetric evaluation of an atlas-based synthetic CT generation approach for MR-only radiotherapy of pelvis anatomy.
Article in Journal of applied clinical medical physics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Clinical application of MR-Linac in tumor radiotherapy: a systematic review.Radiation oncology (London, England) · 2023Pooled it
- Clinical validation of MR imaging time reduction for substitute/synthetic CT generation for prostate MRI-only treatment planning.Physical and engineering sciences in medicine · 2023Trial
- Image-based evaluation of a commercial AI synthetic CT generator for brain and prostate MR-only radiotherapy.Journal of applied clinical medical physics · 2026Article
- Article
- 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
- Synthetic Computed Tomography generation using deep-learning for female pelvic radiotherapy planning.Physics and imaging in radiation oncology · 2025Article
- The use of PET/MRI in radiotherapy.Insights into imaging · 2024Review
- Emergence of MR-Linac in Radiation Oncology: Successes and Challenges of Riding on the MRgRT Bandwagon.Journal of clinical medicine · 2022Article
- A deep learning-based whole-body solution for PET/MRI attenuation correction.EJNMMI physics · 2022Article
- Review
- Artificial intelligence-based bone-enhanced magnetic resonance image-a computed tomography/magnetic resonance image composite image modality in nasopharyngeal carcinoma radiotherapy.Quantitative imaging in medicine and surgery · 2021Article
- Synthetic digital reconstructed radiographs for MR-only robotic stereotactic radiation therapy: A proof of concept.Computers in biology and medicine · 2021Article
- Deep learning-based synthetic CT generation for MR-only radiotherapy of prostate cancer patients with 0.35T MRI linear accelerator.Journal of applied clinical medical physics · 2021Article
- Male pelvic CT multi-organ segmentation using synthetic MRI-aided dual pyramid networks.Physics in medicine and biology · 2021Article
- An Indirect Multimodal Image Registration and Completion Method Guided by Image Synthesis.Computational and mathematical methods in medicine · 2020Article
- Assessment of dosimetric and positioning accuracy of a magnetic resonance imaging-only solution for external beam radiotherapy of pelvic anatomy.Physics and imaging in radiation oncology · 2019Article
- Geometric and dosimetric impact of anatomical changes for MR-only radiation therapy for the prostate.Journal of applied clinical medical physics · 2019Article
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4 authors.
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Abstract
purposeTo investigate the potential of an atlas-based approach in generation of synthetic CT for pelvis anatomy.
methodsTwenty-three matched pairs of computed tomography (CT) and magnetic resonance imaging (MRI) scans were selected from a pool of prostate cancer patients. All MR scans were preprocessed to reduce scanner- and patient-induced intensity inhomogeneities and to standardize their intensity histograms. Ten (training dataset) of 23 pairs were then utilized to construct the coregistered CT-MR atlas. The synthetic CT for a new patient is generated by appropriately weighting the deformed atlas of CT-MR onto the new patient MRI. The training dataset was used as an atlas to generate the synthetic CT for the rest of the patients (test dataset). The mean absolute error (MAE) between the deformed planning CT and synthetic CT was computed over the entire CT image, bone, fat, and muscle tissues. The original treatment plans were also recomputed on the new synthetic CTs and dose-volume histogram metrics were compared. The results were compared with a commercially available synthetic CT Software (MRCAT) that is routinely used in our clinic.
resultsMAE errors (±SD) between the deformed planning CT and our proposed synthetic CTs in the test dataset were 47 ± 5, 116 ± 12, 36 ± 6, and 47 ± 5 HU for the entire image, bone, fat, and muscle tissues respectively. The MAEs were 65 ± 5, 172 ± 9, 43 ± 7, and 42 ± 4 HU for the corresponding tissues in MRCAT CT. The dosimetric comparison showed consistent results for all plans using our synthetic CT, deformed planning CT and MRCAT CT.
conclusionWe investigated the potential of a multiatlas approach to generate synthetic CT images for the pelvis. Our results demonstrate excellent results in terms of HU value assignment compared to the original CT and dosimetric consistency.
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