ArticleJournal of Korean Neurosurgical Society2020
Spine Computed Tomography to Magnetic Resonance Image Synthesis Using Generative Adversarial Networks : A Preliminary Study.
Article in Journal of Korean Neurosurgical Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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Who cites it
25 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 and Computer Vision in Low Back Pain: A Systematic Review.International journal of environmental research and public health · 2021Pooled it
- Synthesizing lumbar computed tomography myelography from plain computed tomography images using a cycle-consistent generative adversarial network.North American Spine Society journal · 2026Article
- Review
- CT-to-MRI translation of medical volume data based on an enhanced diffusion model.Scientific reports · 2026Article
- Artificial Intelligence Applications in Musculoskeletal Imaging.Current reviews in musculoskeletal medicine · 2025Review
- Cross-modality image-to-image translation from MR to syntheticNeuroradiology · 2025Article
- Sex estimation with convolutional neural networks using the patella magnetic resonance image slices.Forensic science, medicine, and pathology · 2025Article
- Translation of computed tomography images to T2-Weighted magnetic resonance images of lumbar spine using generative adversarial networks on sagittal images.Scientific reports · 2025Article
- On-board synthetic 4D MRI generation from 4D CBCT for radiotherapy of abdominal tumors: A feasibility study.Medical physics · 2024Article
- Denoising diffusion-based MRI to CT image translation enables automated spinal segmentation.European radiology experimental · 2023Article
- Neurosurgical Management of Cerebrospinal Tumors in the Era of Artificial Intelligence : A Scoping Review.Journal of Korean Neurosurgical Society · 2023Article
- The promise and limitations of artificial intelligence in musculoskeletal imaging.Frontiers in radiology · 2023Review
- Image Translation by Ad CycleGAN for COVID-19 X-Ray Images: A New Approach for Controllable GAN.Sensors (Basel, Switzerland) · 2022Article
- Artificial intelligence and spine imaging: limitations, regulatory issues and future direction.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2022Review
- Genetic Odyssey to Ossification of the Posterior Longitudinal Ligament in the Cervical Spine: A Systematic Review.Neurospine · 2022Article
- Conversational Artificial Intelligence for Spinal Pain Questionnaire: Validation and User Satisfaction.Neurospine · 2022Article
- Lumbar Spine Computed Tomography to Magnetic Resonance Imaging Synthesis Using Generative Adversarial Network: Visual Turing Test.Diagnostics (Basel, Switzerland) · 2022Article
- AI musculoskeletal clinical applications: how can AI increase my day-to-day efficiency?Skeletal radiology · 2022Review
- Machine Learning Algorithms in Neuroimaging: An Overview.Acta neurochirurgica. Supplement · 2022Review
Corrections and comments
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Authors and funding
9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
objectiveTo generate synthetic spine magnetic resonance (MR) images from spine computed tomography (CT) using generative adversarial networks (GANs), as well as to determine the similarities between synthesized and real MR images.
methodsGANs were trained to transform spine CT image slices into spine magnetic resonance T2 weighted (MRT2) axial image slices by combining adversarial loss and voxel-wise loss. Experiments were performed using 280 pairs of lumbar spine CT scans and MRT2 images. The MRT2 images were then synthesized from 15 other spine CT scans. To evaluate whether the synthetic MR images were realistic, two radiologists, two spine surgeons, and two residents blindly classified the real and synthetic MRT2 images. Two experienced radiologists then evaluated the similarities between subdivisions of the real and synthetic MRT2 images. Quantitative analysis of the synthetic MRT2 images was performed using the mean absolute error (MAE) and peak signal-to-noise ratio (PSNR).
resultsThe mean overall similarity of the synthetic MRT2 images evaluated by radiologists was 80.2%. In the blind classification of the real MRT2 images, the failure rate ranged from 0% to 40%. The MAE value of each image ranged from 13.75 to 34.24 pixels (mean, 21.19 pixels), and the PSNR of each image ranged from 61.96 to 68.16 dB (mean, 64.92 dB).
conclusionThis was the first study to apply GANs to synthesize spine MR images from CT images. Despite the small dataset of 280 pairs, the synthetic MR images were relatively well implemented. Synthesis of medical images using GANs is a new paradigm of artificial intelligence application in medical imaging. We expect that synthesis of MR images from spine CT images using GANs will improve the diagnostic usefulness of CT. To better inform the clinical applications of this technique, further studies are needed involving a large dataset, a variety of pathologies, and other MR sequence of the lumbar spine.
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