ArticleFrontiers in oncology2019
Bulk Anatomical Density Based Dose Calculation for Patient-Specific Quality Assurance of MRI-Only Prostate Radiotherapy.
Article in Frontiers in oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A Review of the Australian MRI Linac Program: From Pie in the Sky to Research Milestone.Journal of medical imaging and radiation oncology · 2026Review
- Brain MR-only workflow in clinical practice: A comparison among generators for quality assurance and patient positioning.Journal of applied clinical medical physics · 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
- Channel-wise attention enhanced and structural similarity constrained cycleGAN for effective synthetic CT generation from head and neck MRI images.Radiation oncology (London, England) · 2024Article
- MRI-Guided Radiation Therapy for Prostate Cancer: The Next Frontier in Ultrahypofractionation.Cancers · 2023Review
- Structurally-constrained optical-flow-guided adversarial generation of synthetic CT for MR-only radiotherapy treatment planning.Scientific reports · 2022Article
- Quality assurance for MRI-only radiation therapy: A voxel-wise population-based methodology for image and dose assessment of synthetic CT generation methods.Frontiers in oncology · 2022Article
- Comparison of Synthetic Computed Tomography Generation Methods, Incorporating Male and Female Anatomical Differences, for Magnetic Resonance Imaging-Only Definitive Pelvic Radiotherapy.Frontiers in oncology · 2022Article
- Optimisation and validation of an integrated magnetic resonance imaging-only radiotherapy planning solution.Physics and imaging in radiation oncology · 2021Article
- Magnetic resonance image-based tomotherapy planning for prostate cancer.Radiation oncology journal · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prostate cancer treatment planning can be performed using magnetic resonance imaging (MRI) only with sCT scans. However, sCT scans are computer generated from MRI data and therefore robust, efficient, and accurate patient-specific quality assurance methods for dosimetric verification are required. Bulk anatomical density (BAD) maps can be generated based on anatomical contours derived from the MRI image. This study investigates and optimizes the BAD map approach for sCT quality assurance with a large patient CT and MRI dataset. 3D T2-weighted MRI and full density CT images of 54 patients were used to create BAD maps with different tissue class combinations. Mean Hounsfield units (HU) of Fat (F: below -30 HU), the entire Tissue [T: excluding bone (B)], and Muscle (M: excluding bone and fat) were derived from the CT scans. CT based BAD maps (BAD
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