ArticleEuropean radiology experimental2018
Generative models: an upcoming innovation in musculoskeletal radiology? A preliminary test in spine imaging.
Article in European radiology experimental, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Utility of machine learning algorithms in degenerative cervical and lumbar spine disease: a systematic review.Neurosurgical review · 2022Pooled it
- Artificial Intelligence Technologies in Neurosurgery: a Systematic Literature Review Using Topic Modeling. Part II: Research Objectives and Perspectives.Sovremennye tekhnologii v meditsine · 2021Pooled it
- An ELIXIR scoping review on domain-specific evaluation metrics for synthetic data in life sciences.NAR genomics and bioinformatics · 2026Article
- Diagnostic interchangeability of deep-learning based Synth-STIR images generated from T1 and T2 weighted spine images.European radiology · 2026Article
- Feasibility of generating sagittal radiographs from coronal views using GAN-based deep learning framework in adolescent idiopathic scoliosis.European radiology experimental · 2025Article
- Article
- AI applications in musculoskeletal imaging: a narrative review.European radiology experimental · 2024Review
- Denoising diffusion-based MRI to CT image translation enables automated spinal segmentation.European radiology experimental · 2023Article
- [Deep learning method for magnetic resonance imaging fluid-attenuated inversion recovery image synthesis].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
- Improving accelerated 3D imaging in MRI-guided radiotherapy for prostate cancer using a deep learning method.Radiation oncology (London, England) · 2023Article
- A practical guide to the development and deployment of deep learning models for the orthopedic surgeon: part II.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2023Review
- The promise and limitations of artificial intelligence in musculoskeletal imaging.Frontiers in radiology · 2023Review
- Artificial Intelligence in Spinal Imaging: Current Status and Future Directions.International journal of environmental research and public health · 2022Review
- Synthesizing Quantitative T2 Maps in Right Lateral Knee Femoral Condyles from Multicontrast Anatomic Data with a Conditional Generative Adversarial Network.Radiology. Artificial intelligence · 2021Article
- Generating Virtual Short Tau Inversion Recovery (STIR) Images from T1- and T2-Weighted Images Using a Conditional Generative Adversarial Network in Spine Imaging.Diagnostics (Basel, Switzerland) · 2021Article
- Deep Generative Adversarial Networks: Applications in Musculoskeletal Imaging.Radiology. Artificial intelligence · 2021Review
- A review on medical imaging synthesis using deep learning and its clinical applications.Journal of applied clinical medical physics · 2021Review
- Deep generative models for automated muscle segmentation in computed tomography scanning.PloS one · 2021Article
- Automated interpretation of the coronary angioscopy with deep convolutional neural networks.Open heart · 2020Article
- Current applications and future directions of deep learning in musculoskeletal radiology.Skeletal radiology · 2020Review
Corrections and comments
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7 authors.
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Abstract
backgroundDeep learning is a ground-breaking technology that is revolutionising many research and industrial fields. Generative models are recently gaining interest. Here, we investigate their potential, namely conditional generative adversarial networks, in the field of magnetic resonance imaging (MRI) of the spine, by performing clinically relevant benchmark cases.
methodsFirst, the enhancement of the resolution of T2-weighted (T2W) images (super-resolution) was tested. Then, automated image-to-image translation was tested in the following tasks: (1) from T1-weighted to T2W images of the lumbar spine and (2) vice versa; (3) from T2W to short time inversion-recovery (STIR) images; (4) from T2W to turbo inversion recovery magnitude (TIRM) images; (5) from sagittal standing x-ray projections to T2W images. Clinical and quantitative assessments of the outputs by means of image quality metrics were performed. The training of the models was performed on MRI and x-ray images from 989 patients.
resultsThe performance of the models was generally positive and promising, but with several limitations. The number of disc protrusions or herniations showed good concordance (κ = 0.691) between native and super-resolution images. Moderate-to-excellent concordance was found when translating T2W to STIR and TIRM images (κ ≥ 0.842 regarding disc degeneration), while the agreement was poor when translating x-ray to T2W images.
conclusionsConditional generative adversarial networks are able to generate perceptually convincing synthetic images of the spine in super-resolution and image-to-image translation tasks. Taking into account the limitations of the study, deep learning-based generative methods showed the potential to be an upcoming innovation in musculoskeletal radiology.
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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.