ArticleNeuroradiology2023
Feasibility study of super-resolution deep learning-based reconstruction using k-space data in brain diffusion-weighted images.
Article in Neuroradiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
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- Super-resolution deep learning reconstruction for brain fluid-attenuated inversion recovery: image quality and white matter hyperintensity volume analysis.Neuroradiology · 2026Article
- Super-resolution deep learning reconstruction enhances visualization of cerebral aneurysms on magnetic resonance angiography.Neuroradiology · 2026Article
- Super-resolution deep learning reconstruction improves brain MRI quality and detection of metastases.Japanese journal of radiology · 2026Article
- Super-Resolution Deep Learning Reconstruction for T2*-Weighted Images: Improvement in Microbleed Lesion Detection and Image Quality.Journal of imaging informatics in medicine · 2026Article
- Deep learning-based super-resolution dynamic contrast-enhanced radiomics model for predicting NSMP endometrial cancer.Frontiers in oncology · 2026Article
- Super-resolution deep learning reconstruction to evaluate lumbar spinal stenosis status on magnetic resonance myelography.Japanese journal of radiology · 2025Article
- The Expanding Frontier: The Role of Artificial Intelligence in Pediatric Neuroradiology.Children (Basel, Switzerland) · 2025Review
- Evaluating Second-Generation Deep Learning Technique for Noise Reduction in Myocardial T1-Mapping Magnetic Resonance Imaging.Diseases (Basel, Switzerland) · 2025Article
- Application of deep learning techniques for breath-hold, high-precision T2-weighted magnetic resonance imaging of the abdomen.Abdominal radiology (New York) · 2025Article
- High-precision MRI of liver and hepatic lesions on gadoxetic acid-enhanced hepatobiliary phase using a deep learning technique.Japanese journal of radiology · 2025Article
- Deep learning reconstruction of diffusion-weighted brain MRI for evaluation of patients with acute neurologic symptoms.Scientific reports · 2024Article
- Super-resolution Deep Learning Reconstruction Cervical Spine 1.5T MRI: Improved Interobserver Agreement in Evaluations of Neuroforaminal Stenosis Compared to Conventional Deep Learning Reconstruction.Journal of imaging informatics in medicine · 2024Article
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12 authors.
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Abstract
purposeThe purpose of this study is to evaluate the influence of super-resolution deep learning-based reconstruction (SR-DLR), which utilizes k-space data, on the quality of images and the quantitation of the apparent diffusion coefficient (ADC) for diffusion-weighted images (DWI) in brain magnetic resonance imaging (MRI).
methodsA retrospective analysis was performed on 34 patients who had undergone DWI using a 3 T MRI system with SR-DLR reconstruction based on k-space data in August 2022. DWI was reconstructed with SR-DLR (Matrix = 684 × 684) and without SR-DLR (Matrix = 228 × 228). Measurements were made of the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) in white matter (WM) and grey matter (GM), and the full width at half maximum (FWHM) of the septum pellucidum. Two radiologists assessed image noise, contrast, artifacts, blur, and the overall quality of three image types using a four-point scale. Quantitative and qualitative scores between images with and without SR-DLR were compared using the Wilcoxon signed-rank test.
resultsImages with SR-DLR showed significantly higher SNRs and CNRs than those without SR-DLR (p < 0.001). No statistically significant variances were found in the apparent diffusion coefficients (ADCs) in WM and GM between images with and without SR-DLR (ADC in WM, p = 0.945; ADC in GM, p = 0.235). Moreover, the FWHM without SR-DLR was notably lower compared to that with SR-DLR (p < 0.001).
conclusionSR-DLR has the potential to augment the quality of DWI in DL MRI scans without significantly impacting ADC quantitation.
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