ArticleFrontiers in oncology2021
CT-Based Pelvic T
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Online adaptive radiotherapy: International strategies for AI-enabled workflow efficiency and radiation therapist-led delivery for sustainable practice.Technical innovations & patient support in radiation oncology · 2026Article
- Mask-Guided and Fidelity-Constrained Deep Learning Model for Accurate Translation of Brain CT Images to Diffusion MRI Images in Acute Stroke Patients.Journal of imaging informatics in medicine · 2026Article
- Generating synthetic CEM from low-energy images using deep learning: A future without contrast media? A proof-of-concept study.European radiology experimental · 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
- [Stroke-p2pHD: Cross-modality generation model of cerebral infarction from CT to DWI images].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- The intelligent development and preservation of folk sports culture under artificial intelligence.Scientific reports · 2025Article
- Evaluation of AI-based auto-contouring tools in radiotherapy: A single-institution study.Journal of applied clinical medical physics · 2025Article
- Domain-Adaptive and Per-Fraction Guided Deep Learning Framework for Magnetic Resonance Imaging-Based Segmentation of Organs at Risk in Gynecologic Cancers.Advances in radiation oncology · 2025Article
- Synthetic CT generation from CBCT and MRI using StarGAN in the Pelvic Region.Radiation oncology (London, England) · 2025Article
- Diffusion-/perfusion-weighted imaging fusion to automatically identify stroke within 4.5 h.European radiology · 2024Article
- Insights and Considerations in Development and Performance Evaluation of Generative Adversarial Networks (GANs): What Radiologists Need to Know.Diagnostics (Basel, Switzerland) · 2024Review
- CrossMagma (New York, N.Y.) · 2024Article
- Segmentation of pelvic structures in T2 MRI via MR-to-CT synthesis.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2024Article
- Machine Learning for Medical Image Translation: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- Addressing inter-device variations in optical coherence tomography angiography: will image-to-image translation systems help?International journal of retina and vitreous · 2023Article
- Non-contrast CT synthesis using patch-based cycle-consistent generative adversarial network (Cycle-GAN) for radiomics and deep learning in the era of COVID-19.Scientific reports · 2023Article
- Article
- Dose distribution prediction for head-and-neck cancer radiotherapy using a generative adversarial network: influence of input data.Frontiers in oncology · 2023Article
- Towards 2D/3D Registration of the Preoperative MRI to Intraoperative Fluoroscopic Images for Visualization of Bone Defects.Computer methods in biomechanics and biomedical engineering. Imaging & visualization · 2023Article
- Synthesis of magnetic resonance images from computed tomography data using convolutional neural network with contextual loss function.Quantitative imaging in medicine and surgery · 2022Article
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Authors and funding
10 authors.
Funding
Abstract
backgroundComputed tomography (CT) and magnetic resonance imaging (MRI) are the mainstay imaging modalities in radiotherapy planning. In MR-Linac treatment, manual annotation of organs-at-risk (OARs) and clinical volumes requires a significant clinician interaction and is a major challenge. Currently, there is a lack of available pre-annotated MRI data for training supervised segmentation algorithms. This study aimed to develop a deep learning (DL)-based framework to synthesize pelvic T
methodsMRI synthesis was performed using UNet++ and cycle-consistent generative adversarial network (Cycle-GAN), and the predictions were compared qualitatively and quantitatively against a baseline UNet model using pixel-wise and perceptual loss functions. Additionally, the Cycle-GAN predictions were evaluated through qualitative expert testing (4 radiologists), and a pelvic bone segmentation routine based on a UNet architecture was trained on synthetic MRI using CT-propagated contours and subsequently tested on real pelvic T
resultsIn our experiments, Cycle-GAN generated sharp images for all pelvic slices whilst UNet and UNet++ predictions suffered from poorer spatial resolution within deformable soft-tissues (e.g. bladder, bowel). Qualitative radiologist assessment showed inter-expert variabilities in the test scores; each of the four radiologists correctly identified images as acquired/synthetic with 67%, 100%, 86% and 94% accuracy. Unsupervised segmentation of pelvic bone on T1-weighted images was successful in a number of test cases.
conclusionPelvic MRI synthesis is a challenging task due to the absence of soft-tissue contrast on CT. Our study showed the potential of deep learning models for synthesizing realistic MR images from CT, and transferring cross-domain knowledge which may help to expand training datasets for 21 development of MR-only segmentation models.
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