ArticleDiagnostics (Basel, Switzerland)2021
Generating Virtual Short Tau Inversion Recovery (STIR) Images from T1- and T2-Weighted Images Using a Conditional Generative Adversarial Network in Spine Imaging.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Seeing the Risk in Virtual Fat-Suppressed Spine MRI: External Benchmarking and Uncertainty-Guided Selective Risk Analysis.Journal of imaging informatics in medicine · 2026Article
- Artificial Intelligence for Detection and Characterisation of Bone Metastases on MRI: A Scoping Review.Cancers · 2026Review
- Clinical applications of MRI-based artificial intelligence in spinal metastases: A systematic review.Journal of bone oncology · 2026Review
- Generating synthetic high-resolution spinal STIR and T1w images from T2w FSE and low-resolution axial Dixon.European radiology · 2025Article
- AI-Generated Synthetic STIR of the Lumbar Spine from T1 and T2 MRI Sequences Trained with Open-Source Algorithms.AJNR. American journal of neuroradiology · 2025Article
- Article
- Denoising diffusion-based MRI to CT image translation enables automated spinal segmentation.European radiology experimental · 2023Article
- One Model to Synthesize Them All: Multi-Contrast Multi-Scale Transformer for Missing Data Imputation.IEEE transactions on medical imaging · 2023Article
- Deep Learning-Generated Synthetic MR Imaging STIR Spine Images Are Superior in Image Quality and Diagnostically Equivalent to Conventional STIR: A Multicenter, Multireader Trial.AJNR. American journal of neuroradiology · 2023Article
- Implementation of GAN-Based, Synthetic T2-Weighted Fat Saturated Images in the Routine Radiological Workflow Improves Spinal Pathology Detection.Diagnostics (Basel, Switzerland) · 2023Article
- [Postoperative imaging of the musculoskeletal system - spine].Radiologie (Heidelberg, Germany) · 2022Review
- Deep learning-based convolutional neural network for intramodality brain MRI synthesis.Journal of applied clinical medical physics · 2022Article
- AUE-Net: Automated Generation of Ultrasound Elastography Using Generative Adversarial Network.Diagnostics (Basel, Switzerland) · 2022Article
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Authors and funding
14 authors.
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Abstract
Short tau inversion recovery (STIR) sequences are frequently used in magnetic resonance imaging (MRI) of the spine. However, STIR sequences require a significant amount of scanning time. The purpose of the present study was to generate virtual STIR (vSTIR) images from non-contrast, non-fat-suppressed T1- and T2-weighted images using a conditional generative adversarial network (cGAN). The training dataset comprised 612 studies from 514 patients, and the validation dataset comprised 141 studies from 133 patients. For validation, 100 original STIR and respective vSTIR series were presented to six senior radiologists (blinded for the STIR type) in independent A/B-testing sessions. Additionally, for 141 real or vSTIR sequences, the testers were required to produce a structured report of 15 different findings. In the A/B-test, most testers could not reliably identify the real STIR (mean error of tester 1-6: 41%; 44%; 58%; 48%; 39%; 45%). In the evaluation of the structured reports, vSTIR was equivalent to real STIR in 13 of 15 categories. In the category of the number of STIR hyperintense vertebral bodies (
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