ArticleMedical physics2018
Generating synthetic CTs from magnetic resonance images using generative adversarial networks.
Article in Medical physics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 96 papers, 2 of them syntheses that pooled it.
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Who cites it
96 citing papers in PubMed, 2 syntheses or guidelines pooled it, 292 citations in OpenAlex.
- A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy.Frontiers in radiology · 2024Pooled it
- 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
- Optimizing the delivery of radiotherapy with artificial intelligence.Nature reviews. Clinical oncology · 2026Review
- Image-based evaluation of a commercial AI synthetic CT generator for brain and prostate MR-only radiotherapy.Journal of applied clinical medical physics · 2026Article
- Application of generative artificial intelligence (AI) to support pain neuroscience research in persons who are pregnant.Communications medicine · 2026Article
- Improved synthetic CT generation using surface scan information integrated into a deformable registration algorithm for limited Field of View data.Physics and imaging in radiation oncology · 2026Article
- Evaluation of Magnetic Resonance Imaging-Derived Synthetic Computed Tomography in Prostate Cancer Carbon Ion Radiotherapy: Impact of Fixation Shell.International journal of particle therapy · 2026Article
- Simulation-free workflow for lattice radiation therapy using deep learning predicted synthetic computed tomography: A feasibility study.Journal of applied clinical medical physics · 2025Article
- CBCT Reconstruction Using Single X-Ray Projection With Cycle-Domain Geometry-Integrated Denoising Diffusion Probabilistic Models.IEEE transactions on medical imaging · 2025Article
- Deep Learning-Based Synthetic Computed Tomography for Low-Field Brain Magnetic Resonance-Guided Radiation Therapy.International journal of radiation oncology, biology, physics · 2025Article
- A deep learning algorithm to generate synthetic computed tomography images for brain treatments from 0.35 T magnetic resonance imaging.Physics and imaging in radiation oncology · 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
- Image-Based Generative Artificial Intelligence in Radiology: Comprehensive Updates.Korean journal of radiology · 2024Review
- CT synthesis with deep learning for MR-only radiotherapy planning: a review.Biomedical engineering letters · 2024Review
- Synthetic CT generation based on multi-sequence MR using CycleGAN for head and neck MRI-only planning.Biomedical engineering letters · 2024Article
- Artificial Intelligence-Enhanced Neurocritical Care for Traumatic Brain Injury : Past, Present and Future.Journal of Korean Neurosurgical Society · 2024Article
- LungViT: Ensembling Cascade of Texture Sensitive Hierarchical Vision Transformers for Cross-Volume Chest CT Image-to-Image Translation.IEEE transactions on medical imaging · 2024Article
- Context-Aware Transformer GAN for Direct Generation of Attenuation and Scatter Corrected PET Data.IEEE transactions on radiation and plasma medical sciences · 2024Article
- Article
- Clinical feasibility of deep learning-based synthetic CT images from T2-weighted MR images for cervical cancer patients compared to MRCAT.Scientific reports · 2024Article
36 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors at 2 institutions in 1 country.
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Abstract
purposeWhile MR-only treatment planning using synthetic CTs (synCTs) offers potential for streamlining clinical workflow, a need exists for an efficient and automated synCT generation in the brain to facilitate near real-time MR-only planning. This work describes a novel method for generating brain synCTs based on generative adversarial networks (GANs), a deep learning model that trains two competing networks simultaneously, and compares it to a deep convolutional neural network (CNN).
methodsPost-Gadolinium T1-Weighted and CT-SIM images from fifteen brain cancer patients were retrospectively analyzed. The GAN model was developed to generate synCTs using T1-weighted MRI images as the input using a residual network (ResNet) as the generator. The discriminator is a CNN with five convolutional layers that classified the input image as real or synthetic. Fivefold cross-validation was performed to validate our model. GAN performance was compared to CNN based on mean absolute error (MAE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR) metrics between the synCT and CT images.
resultsGAN training took ~11 h with a new case testing time of 5.7 ± 0.6 s. For GAN, MAEs between synCT and CT-SIM were 89.3 ± 10.3 Hounsfield units (HU) and 41.9 ± 8.6 HU across the entire FOV and tissues, respectively. However, MAE in the bone and air was, on average, ~240-255 HU. By comparison, the CNN model had an average full FOV MAE of 102.4 ± 11.1 HU. For GAN, the mean PSNR was 26.6 ± 1.2 and SSIM was 0.83 ± 0.03. GAN synCTs preserved details better than CNN, and regions of abnormal anatomy were well represented on GAN synCTs.
conclusionsWe developed and validated a GAN model using a single T1-weighted MR image as the input that generates robust, high quality synCTs in seconds. Our method offers strong potential for supporting near real-time MR-only treatment planning in the brain.
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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.