ArticleNeuroradiology2021
A preliminary study of deep learning-based reconstruction specialized for denoising in high-frequency domain: usefulness in high-resolution three-dimensional magnetic resonance cisternography of the cerebellopontine angle.
Article in Neuroradiology, 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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13 citing papers in PubMed, 33 citations in OpenAlex.
- Usefulness of compressed sensing coronary magnetic resonance angiography with deep learning reconstruction.Japanese journal of radiology · 2025Article
- Efficacy of compressed sensing and deep learning reconstruction for adult female pelvic MRI at 1.5 T.European radiology experimental · 2024Article
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- Exploring the impact of super-resolution deep learning on MR angiography image quality.Neuroradiology · 2024Article
- Validation and Feasibility of Ultrafast Cervical Spine MRI Using a Deep Learning-Assisted 3D Iterative Image Enhancement System.Journal of multidisciplinary healthcare · 2024Article
- Feasibility study of super-resolution deep learning-based reconstruction using k-space data in brain diffusion-weighted images.Neuroradiology · 2023Article
- Deep learning-based reconstruction and 3D hybrid profile order technique for MRCP at 3T: evaluation of image quality and acquisition time.European radiology · 2023Article
- Denoising approach with deep learning-based reconstruction for neuromelanin-sensitive MRI: image quality and diagnostic performance.Japanese journal of radiology · 2023Article
- [Near-infrared targeted probe designed for intraoperative imaging of prostatic neurovascular bundles].Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2023Article
- Denoising using deep-learning-based reconstruction for whole-heart coronary MRA with sub-millimeter isotropic resolution at 3 T: a volunteer study.Diagnostic and interventional radiology (Ankara, Turkey) · 2022Article
- Impact of deep learning reconstruction on intracranial 1.5 T magnetic resonance angiography.Japanese journal of radiology · 2022Article
- Applicability of deep learning-based reconstruction trained by brain and knee 3T MRI to lumbar 1.5T MRI.Acta radiologica open · 2021Article
- [Artificial intelligence in oncological radiology : A (p)review].Der Radiologe · 2021Review
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11 authors at 3 institutions in 1 country.
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Abstract
purposeDeep learning-based reconstruction (DLR) has been developed to reduce image noise and increase the signal-to-noise ratio (SNR). We aimed to evaluate the efficacy of DLR for high spatial resolution (HR)-MR cisternography.
methodsThis retrospective study included 35 patients who underwent HR-MR cisternography. The images were reconstructed with or without DLR. The SNRs of the CSF and pons, contrast of the CSF and pons, and sharpness of the normal-side trigeminal nerve using full width at half maximum (FWHM) were compared between the two image types. Noise quality, sharpness, artifacts, and overall image quality of these two types of images were qualitatively scored.
resultsThe SNRs of the CSF and pons were significantly higher with DLR than without DLR (CSF 21.81 ± 7.60 vs. 15.33 ± 4.03, p < 0.001; pons 5.96 ± 1.38 vs. 3.99 ± 0.48, p < 0.001). There were no significant differences in the contrast of the CSF and pons (p = 0.225) and sharpness of the normal-side trigeminal nerve using FWHM (p = 0.185) without and with DLR, respectively. Noise quality and the overall image quality were significantly higher with DLR than without DLR (noise quality 3.95 ± 0.19 vs. 2.53 ± 0.44, p < 0.001; overall image quality 3.97 ± 0.17 vs. 2.97 ± 0.12, p < 0.001). There were no significant differences in sharpness (p = 0.371) and artifacts (p = 1) without and with DLR.
conclusionDLR can improve the image quality of HR-MR cisternography by reducing image noise without sacrificing contrast or sharpness.
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