ArticleJournal of applied clinical medical physics2021
Performance of deep learning synthetic CTs for MR-only brain radiation therapy.
Article in Journal of applied clinical medical physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- Adoption of Hybrid MRI-Linac Systems for the Treatment of Brain Tumors: A Systematic Review of the Current Literature Regarding Clinical and Technical Features.Technology in cancer research & treatmentPooled it
- Synthetic CT artifacts in MR-only brain radiotherapy: Clinical implementation experience.Journal of applied clinical medical physics · 2026Article
- An open-source tool for converting 3D mesh volumes into synthetic DICOM CT images for medical physics research.Physical and engineering sciences in medicine · 2025Article
- Using generative adversarial deep learning networks to synthesize cerebrovascular reactivity imaging from pre-acetazolamide arterial spin labeling in moyamoya disease.Neuroradiology · 2025Article
- Principles of artificial intelligence in radiooncology.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Review
- Brain MR-only workflow in clinical practice: A comparison among generators for quality assurance and patient positioning.Journal of applied clinical medical physics · 2025Article
- CT synthesis with deep learning for MR-only radiotherapy planning: a review.Biomedical engineering letters · 2024Review
- Clinical implementation of a commercial synthetic computed tomography solution for radiotherapy treatment of glioblastoma.Physics and imaging in radiation oncology · 2024Article
- "sCT-Feasibility" - a feasibility study for deep learning-based MRI-only brain radiotherapy.Radiation oncology (London, England) · 2024Article
- Impact of magnetic resonance imaging-related geometric distortion of dose distribution in fractionated stereotactic radiotherapy in patients with brain metastases.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2024Article
- Machine Learning for Medical Image Translation: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- Clinical feasibility of a commercially available MRI-only method for radiotherapy treatment planning of the brain.Journal of applied clinical medical physics · 2023Article
- Synthetic CTs for MRI-only brain RT treatment: integration of immobilization systems.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2023Article
- Synthetic Images Are Here to Stay.Radiology · 2023Article
- Article
- Synthetic cranial MRI from 3D optical surface scans using deep learning for radiation therapy treatment planning.Physical and engineering sciences in medicine · 2023Article
Corrections and comments
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Authors and funding
7 authors at 6 institutions in 1 country.
Funding
Abstract
purposeTo evaluate the dosimetric and image-guided radiation therapy (IGRT) performance of a novel generative adversarial network (GAN) generated synthetic CT (synCT) in the brain and compare its performance for clinical use including conventional brain radiotherapy, cranial stereotactic radiosurgery (SRS), planar, and volumetric IGRT. METHODS AND MATERIALS: SynCT images for 12 brain cancer patients (6 SRS, 6 conventional) were generated from T1-weighted postgadolinium magnetic resonance (MR) images by applying a GAN model with a residual network (ResNet) generator and a convolutional neural network (CNN) with 5 convolutional layers as the discriminator that classified input images as real or synthetic. Following rigid registration, clinical structures and treatment plans derived from simulation CT (simCT) images were transferred to synCTs. Dose was recalculated for 15 simCT/synCT plan pairs using fixed monitor units. Two-dimensional (2D) gamma analysis (2%/2 mm, 1%/1 mm) was performed to compare dose distributions at isocenter. Dose-volume histogram (DVH) metrics (D
resultsAverage gamma passing rates at 1%/1mm and 2%/2mm were 99.0 ± 1.5% and 99.9 ± 0.2%, respectively. Excellent agreement in DVH metrics was observed (mean difference ≤0.10 ± 0.04 Gy for targets, 0.13 ± 0.04 Gy for OARs). The population averaged mean difference in CBCT-synCT registrations were <0.2 mm and 0.1 degree different from simCT-based registrations. The mean difference between kV-synCT DRR and kV-simCT DRR registrations was <0.5 mm with no statistically significant differences observed (P > 0.05). An outlier with a large resection cavity exhibited the worst-case scenario.
conclusionBrain GAN synCTs demonstrated excellent performance for dosimetric and IGRT endpoints, offering potential use in high precision brain cancer therapy.
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