ArticleMedical physics2020
Multimodality image registration in the head-and-neck using a deep learning-derived synthetic CT as a bridge.
Article in Medical physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy.Frontiers in radiology · 2024Pooled it
- Application of artificial intelligence in paediatric oncology imaging.Pediatric radiology · 2026Review
- A feasibility study on multimodal CT-MRI registration using segmentation aid and CoLlAGe feature extraction approach.Abdominal radiology (New York) · 2026Article
- Automated MR-only radiotherapy outperforms CT-based radiotherapy and decreases hands-on time for head-and-neck cancer treatment.Clinical and translational radiation oncology · 2026Article
- Image registration using MR-based synthetic CT (sCT) generated by cycle-consistent adversarial networks.Biomedical engineering letters · 2026Article
- A universal medical imaging modality translation model in brain and head-and-neck radiotherapy.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2026Article
- Deformable registration for nasopharyngeal carcinoma using adaptive mask and weight allocation strategy based CycleFCNs model.Radiation oncology (London, England) · 2025Article
- MUsculo-Skeleton-Aware (MUSA) deep learning for anatomically guided head-and-neck CT deformable registration.Medical image analysis · 2025Article
- Towards full-stack deep learning-empowered data processing pipeline for synchrotron tomography experiments.Innovation (Cambridge (Mass.)) · 2024Review
- Review and recommendations on deformable image registration uncertainties for radiotherapy applications.Physics in medicine and biology · 2023Review
- Comprehensive evaluation of similarity between synthetic and real CT images for nasopharyngeal carcinoma.Radiation oncology (London, England) · 2023Article
- Advances in TEE-Centric Intraprocedural Multimodal Image Guidance for Congenital and Structural Heart Disease.Diagnostics (Basel, Switzerland) · 2023Review
- Machine Learning for Medical Image Translation: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- Deep neural network-based synthetic image digital fluoroscopy using digitally reconstructed tomography.Physical and engineering sciences in medicine · 2023Article
- A transformer-based hierarchical registration framework for multimodality deformable image registration.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2023Article
- Improving accelerated 3D imaging in MRI-guided radiotherapy for prostate cancer using a deep learning method.Radiation oncology (London, England) · 2023Article
- The automatic evaluation of steno-occlusive changes in time-of-flight magnetic resonance angiography of moyamoya patients using a 3D coordinate attention residual network.Quantitative imaging in medicine and surgery · 2023Article
- Abdominopelvic MR to CT registration using a synthetic CT intermediate.Journal of applied clinical medical physics · 2022Article
- Plant Genotype to Phenotype Prediction Using Machine Learning.Frontiers in genetics · 2022Review
- Using neural networks to extend cropped medical images for deformable registration among images with differing scan extents.Medical physics · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
purposeTo develop and demonstrate the efficacy of a novel head-and-neck multimodality image registration technique using deep-learning-based cross-modality synthesis. METHODS AND MATERIALS: Twenty-five head-and-neck patients received magnetic resonance (MR) and computed tomography (CT) (CT
resultsWhen large initial rigid misalignment is present, registering CT to MRI-derived synthetic CT aligns the cord better than a direct registration. The average landmark error decreased from 9.8 ± 3.1 mm in MR→CT
conclusionsWe showed that using a deep learning-derived synthetic CT in lieu of an MR for MR→CT and CT→MR deformable registration offers superior results to direct multimodal registration.
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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.