ArticleCancers2021
Feasibility of Synthetic Computed Tomography Images Generated from Magnetic Resonance Imaging Scans Using Various Deep Learning Methods in the Planning of Radiation Therapy for Prostate Cancer.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- RadiolGAN: Multicenter Feasibility Study of Synthetic CT From 3D Ultra-Short Echo Time MRI for Enhanced Pulmonary Radiologic Sign Visualization.Journal of magnetic resonance imaging : JMRI · 2026Article
- Celecoxib potentiates ribociclib-induced apoptosis and anti-tumor activity by co-targeting COX-2/NF-κB and PI3K/AKT/mTOR pathways in hormone receptor-positive/HER2-negative breast cancer.Apoptosis : an international journal on programmed cell death · 2026Article
- Dental metal artifacts in magnetic resonance-based synthetic computed tomography for brain radiotherapy: Impact on dose, patient setup, and geometric distortion.Physics and imaging in radiation oncology · 2026Article
- An overview of utilizing artificial intelligence in localized prostate cancer imaging.Expert review of medical devices · 2025Review
- 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
- 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
- Machine Learning for Medical Image Translation: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- Article
- Synthetic computed tomography generation for abdominal adaptive radiotherapy using low-field magnetic resonance imaging.Physics and imaging in radiation oncology · 2023Article
- Article
- Synthetic 18F-FDG PET Image Generation Using a Combination of Biomathematical Modeling and Machine Learning.Cancers · 2022Article
- Improving the clinical workflow of a MR-Linac by dosimetric evaluation of synthetic CT.Frontiers in oncology · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We aimed to evaluate and compare the qualities of synthetic computed tomography (sCT) generated by various deep-learning methods in volumetric modulated arc therapy (VMAT) planning for prostate cancer. Simulation computed tomography (CT) and T2-weighted simulation magnetic resonance image from 113 patients were used in the sCT generation by three deep-learning approaches: generative adversarial network (GAN), cycle-consistent GAN (CycGAN), and reference-guided CycGAN (RgGAN), a new model which performed further adjustment of sCTs generated by CycGAN with available paired images. VMAT plans on the original simulation CT images were recalculated on the sCTs and the dosimetric differences were evaluated. For soft tissue, a significant difference in the mean Hounsfield unites (HUs) was observed between the original CT images and only sCTs from GAN (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.