ArticleActa radiologica open2021
Applicability of deep learning-based reconstruction trained by brain and knee 3T MRI to lumbar 1.5T MRI.
Article in Acta radiologica open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Application of Deep Learning-Based Reconstruction to Magnetic Resonance Imaging of Canine Stifle Joint in Healthy Beagles: Achieving Enhanced Image Quality With Reduced Slice Thickness.Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association · 2026Article
- Artificial Intelligence in Spine Neuroimaging: Diagnostic and Prognostic Utility of Novel Biomarkers in Lower Back Pain.Journal of clinical medicine · 2026Review
- Image quality evaluation of neonatal brain magnetic resonance imaging using a deep learning reconstruction algorithm: A quantitative and multireader study using variable denoising levels at 3 tesla.Journal of clinical imaging science · 2026Article
- Practical signal-to-noise ratio mapping using single clinical MR images.Radiological physics and technology · 2025Article
- Usefulness of compressed sensing coronary magnetic resonance angiography with deep learning reconstruction.Japanese journal of radiology · 2025Article
- A Comparative Study of Consistency on 1.5-T to 3.0-T Magnetic Resonance Imaging Conversion.Current medical imaging · 2025Article
- Efficacy of compressed sensing and deep learning reconstruction for adult female pelvic MRI at 1.5 T.European radiology experimental · 2024Article
- Bone Imaging of the Knee Using Short-Interval Delta Ultrashort Echo Time and Field Echo Imaging.Journal of clinical medicine · 2024Article
- Validation and Feasibility of Ultrafast Cervical Spine MRI Using a Deep Learning-Assisted 3D Iterative Image Enhancement System.Journal of multidisciplinary healthcare · 2024Article
- Image quality and lesion detectability of deep learning-accelerated T2-weighted Dixon imaging of the cervical spine.Skeletal radiology · 2023Article
- Deep learning reconstruction for the evaluation of neuroforaminal stenosis using 1.5T cervical spine MRI: comparison with 3T MRI without deep learning reconstruction.Neuroradiology · 2022Article
- Impact of deep learning reconstruction on intracranial 1.5 T magnetic resonance angiography.Japanese journal of radiology · 2022Article
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7 authors.
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Abstract
backgroundSeveral deep learning-based methods have been proposed for addressing the long scanning time of magnetic resonance imaging. Most are trained using brain 3T magnetic resonance images, but is unclear whether performance is affected when applying these methods to different anatomical sites and at different field strengths. PURPOSE: To validate the denoising performance of deep learning-based reconstruction method trained by brain and knee 3T magnetic resonance images when applied to lumbar 1.5T magnetic resonance images. MATERIAL AND
methodsUsing a 1.5T scanner, we obtained lumber T2-weighted sequences in 10 volunteers using three different scanning times: 228 s (standard), 119 s (double-fast), and 68 s (triple-fast). We compared the images obtained by the standard sequence with those obtained by the deep learning-based reconstruction-applied faster sequences.
resultsSignal-to-noise ratio values were significantly higher for deep learning-based reconstruction-double-fast than for standard and did not differ significantly between deep learning-based reconstruction-triple-fast and standard. Contrast-to-noise ratio values also did not differ significantly between deep learning-based reconstruction-triple-fast and standard. Qualitative scores for perceived signal-to-noise ratio and overall image quality were significantly higher for deep learning-based reconstruction-double fast and deep learning-based reconstruction-triple-fast than for standard. Average scores for sharpness, contrast, and structure visibility were equal to or higher for deep learning-based reconstruction-double-fast and deep learning-based reconstruction-triple-fast than for standard, but the differences were not statistically significant. The average scores for artifact were lower for deep learning-based reconstruction-double-fast and deep learning-based reconstruction-triple-fast than for standard, but the differences were not statistically significant.
conclusionThe deep learning-based reconstruction method trained by 3T brain and knee images may reduce the scanning time of 1.5T lumbar magnetic resonance images by one-third without sacrificing image quality.
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