ArticleThe British journal of radiology2019
MRI-based treatment planning for liver stereotactic body radiotherapy: validation of a deep learning-based synthetic CT generation method.
Article in The British journal of radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it, 72 citations in OpenAlex.
- Deep learning in MRI-guided radiation therapy: A systematic review.Journal of applied clinical medical physics · 2024Pooled it
- Deep learning-based generation of synthetic multiphasic MRI in hepatocellular carcinoma and cirrhosis.JHEP reports : innovation in hepatology · 2026Article
- Generation of multimodal realistic computational phantoms as a test-bed for validating deep learning-based cross-modality synthesis techniques.Medical & biological engineering & computing · 2026Article
- Innovative approaches in precision radiation oncology: advanced imaging technologies and challenges which shape the future of radiation therapy.Frontiers in medicine · 2025Review
- Dosimetric evaluations using cycle-consistent generative adversarial network synthetic CT for MR-guided adaptive radiation therapy.Frontiers in oncology · 2025Article
- Diffeomorphic transformer-based abdomen MRI-CT deformable image registration.Medical physics · 2024Article
- Article
- Shortening image registration time using a deep neural network for patient positional verification in radiotherapy.Physical and engineering sciences in medicine · 2023Article
- Comprehensive evaluation of similarity between synthetic and real CT images for nasopharyngeal carcinoma.Radiation oncology (London, England) · 2023Article
- Artificial general intelligence for radiation oncology.Meta-radiology · 2023Article
- Comparative study of sub-second temporal resolution 4D-MRI and 4D-CT for target motion assessment in a phantom model.Scientific reports · 2023Article
- Machine Learning for Medical Image Translation: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- Compensation cycle consistent generative adversarial networks (Comp-GAN) for synthetic CT generation from MR scans with truncated anatomy.Medical physics · 2023Article
- Article
- Interobserver Variability Prediction of Primary Gross Tumor in a Patient with Non-Small Cell Lung Cancer.Cancers · 2022Article
- Generating Synthesized Ultrasound Biomicroscopy Images from Anterior Segment Optical Coherent Tomography Images by Generative Adversarial Networks for Iridociliary Assessment.Ophthalmology and therapy · 2022Article
- Stereotactic body radiation therapy for hepatocellular carcinoma: From infancy to ongoing maturity.JHEP reports : innovation in hepatology · 2022Review
- Development of an anthropomorphic multimodality pelvic phantom for quantitative evaluation of a deep-learning-based synthetic computed tomography generation technique.Journal of applied clinical medical physics · 2022Article
- Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities.World journal of gastrointestinal oncology · 2022Review
- Magnetic Resonance-Based Synthetic Computed Tomography Using Generative Adversarial Networks for Intracranial Tumor Radiotherapy Treatment Planning.Journal of personalized medicine · 2022Article
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveThe purpose of this work is to develop and validate a learning-based method to derive electron density from routine anatomical MRI for potential MRI-based SBRT treatment planning.
methodsWe proposed to integrate dense block into cycle generative adversarial network (GAN) to effectively capture the relationship between the CT and MRI for CT synthesis. A cohort of 21 patients with co-registered CT and MR pairs were used to evaluate our proposed method by the leave-one-out cross-validation. Mean absolute error, peak signal-to-noise ratio and normalized cross-correlation were used to quantify the imaging differences between the synthetic CT (sCT) and CT. The accuracy of Hounsfield unit (HU) values in sCT for dose calculation was evaluated by comparing the dose distribution in sCT-based and CT-based treatment planning. Clinically relevant dose-volume histogram metrics were then extracted from the sCT-based and CT-based plans for quantitative comparison.
resultsThe mean absolute error, peak signal-to-noise ratio and normalized cross-correlation of the sCT were 72.87 ± 18.16 HU, 22.65 ± 3.63 dB and 0.92 ± 0.04, respectively. No significant differences were observed in the majority of the planning target volume and organ at risk dose-volume histogram metrics (
conclusionThe image similarity and dosimetric agreement between sCT and original CT warrant further development of an MRI-only workflow for liver stereotactic body radiation therapy. ADVANCES IN KNOWLEDGE: This work is the first deep-learning-based approach to generating abdominal sCT through dense-cycle-GAN. This method can successfully generate the small bony structures such as the rib bones and is able to predict the HU values for dose calculation with comparable accuracy to reference CT images.
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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.