ArticleBiomedical engineering letters2021
Synthetic CT generation from weakly paired MR images using cycle-consistent GAN for MR-guided radiotherapy.
Article in Biomedical engineering letters, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 41 citations in OpenAlex.
- CT image generation from ultrashort echo time MRI for dosimetric assessment in lung cancer radiotherapy.Physics and imaging in radiation oncology · 2026Article
- Development of head-to-head and longitudinal CycleGAN algorithm for MRI harmonization: validation in follow-up MRI evaluation in patients with brain metastasis.Scientific reports · 2026Article
- Generative Models for Medical Image Creation and Translation: A Scoping Review.Sensors (Basel, Switzerland) · 2026Article
- Dose Prediction Deep Learning-Based Model for VMAT of Prostate Cancer Applying Magnetic Resonance Image (MRI) in Versa HD Linear Accelerator.Advanced biomedical research · 2026Article
- A comprehensive comparative study of generative adversarial network architectures for synthetic computed tomography generation in the abdomen.Medical physics · 2025Article
- Dosimetric evaluations using cycle-consistent generative adversarial network synthetic CT for MR-guided adaptive radiation therapy.Frontiers in oncology · 2025Article
- A comprehensive review on Compton camera image reconstruction: from principles to AI innovations.Biomedical engineering letters · 2024Review
- CT synthesis with deep learning for MR-only radiotherapy planning: a review.Biomedical engineering letters · 2024Review
- Clinical feasibility of deep learning-based synthetic CT images from T2-weighted MR images for cervical cancer patients compared to MRCAT.Scientific reports · 2024Article
- 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
- Artificial general intelligence for radiation oncology.Meta-radiology · 2023Article
- Machine Learning for Medical Image Translation: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- Automated deep learning auto-segmentation of air volumes for MRI-guided online adaptive radiation therapy of abdominal tumors.Physics in medicine and biology · 2023Article
- Voxel-Based Internal Dosimetry forNuclear medicine and molecular imaging · 2023Article
- Article
- Synthetic computed tomography for low-field magnetic resonance-guided radiotherapy in the abdomen.Physics and imaging in radiation oncology · 2022Article
- 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
- Synthetic CT Generation from MRI Using Generative Adversarial Networks for MR-Only Brain Radiotherapy Planning.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although MR-guided radiotherapy (MRgRT) is advancing rapidly, generating accurate synthetic CT (sCT) from MRI is still challenging. Previous approaches using deep neural networks require large dataset of precisely co-registered CT and MRI pairs that are difficult to obtain due to respiration and peristalsis. Here, we propose a method to generate sCT based on deep learning training with weakly paired CT and MR images acquired from an MRgRT system using a cycle-consistent GAN (CycleGAN) framework that allows the unpaired image-to-image translation in abdomen and thorax. Data from 90 cancer patients who underwent MRgRT were retrospectively used. CT images of the patients were aligned to the corresponding MR images using deformable registration, and the deformed CT (dCT) and MRI pairs were used for network training and testing. The 2.5D CycleGAN was constructed to generate sCT from the MRI input. To improve the sCT generation performance, a perceptual loss that explores the discrepancy between high-dimensional representations of images extracted from a well-trained classifier was incorporated into the CycleGAN. The CycleGAN with perceptual loss outperformed the U-net in terms of errors and similarities between sCT and dCT, and dose estimation for treatment planning of thorax, and abdomen. The sCT generated using CycleGAN produced virtually identical dose distribution maps and dose-volume histograms compared to dCT. CycleGAN with perceptual loss outperformed U-net in sCT generation when trained with weakly paired dCT-MRI for MRgRT. The proposed method will be useful to increase the treatment accuracy of MR-only or MR-guided adaptive radiotherapy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13534-021-00195-8.
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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.