ArticleScientific reports2020
Deep-learning-based image quality enhancement of compressed sensing magnetic resonance imaging of vessel wall: comparison of self-supervised and unsupervised approaches.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Deep Learning-Based Denoising for High-Resolution Carotid Vessel Wall MRI Using Standard Neurovascular Coils.Magnetic resonance in medicine · 2026Article
- AI-driven radiomics in vascular disease: a narrative review of image analysis and clinical translation.Cardiovascular diagnosis and therapy · 2026Review
- A bias field correction workflow based on generative adversarial network for abdominal cancers treated with 0.35T MR-LINAC.Journal of applied clinical medical physics · 2026Article
- Quantitative analysis of lipofuscin in neurodegenerative diseases using serial sectioning two-photon microscopy and fluorescence lifetime imaging microscopy.Neurophotonics · 2025Article
- The Laryngovibrogram as a normalized spatiotemporal representation of vocal fold dynamics.Scientific reports · 2025Article
- DANTE-CAIPI Accelerated Contrast-Enhanced 3D T1: Deep Learning-Based Image Quality Improvement for Vessel Wall MRI.AJNR. American journal of neuroradiology · 2025Article
- Enhancing Ultrasound Image Quality Across Disease Domains: Application of Cycle-Consistent Generative Adversarial Network and Perceptual Loss.JMIR biomedical engineering · 2024Article
- Deep Learning-Based Reconstruction of 3D T1 SPACE Vessel Wall Imaging Provides Improved Image Quality with Reduced Scan Times: A Preliminary Study.AJNR. American journal of neuroradiology · 2024Article
- ISRSL0: compressed sensing MRI with image smoothness regularized-smoothed [Formula: see text] norm.Scientific reports · 2024Article
- Deep learning improves quality of intracranial vessel wall MRI for better characterization of potentially culprit plaques.Scientific reports · 2024Article
- MRI in the Evaluation of Cryptogenic Stroke and Embolic Stroke of Undetermined Source.Radiology · 2024Review
- Multimodal data integration for predicting progression risk in castration-resistant prostate cancer using deep learning: a multicenter retrospective study.Frontiers in oncology · 2024Article
- Multi-Scale Label-Free Human Brain Imaging with Integrated Serial Sectioning Polarization Sensitive Optical Coherence Tomography and Two-Photon Microscopy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Comparative study of the quantitative accuracy of oncological PET imaging based on deep learning methods.Quantitative imaging in medicine and surgery · 2023Article
- Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging-State-of-the-Art and Challenges.Journal of digital imaging · 2023Review
- Article
- Super-Resolution Ultrasound Imaging Scheme Based on a Symmetric Series Convolutional Neural Network.Sensors (Basel, Switzerland) · 2022Article
- MRI-Based Classification of Neuropsychiatric Systemic Lupus Erythematosus Patients With Self-Supervised Contrastive Learning.Frontiers in neuroscience · 2022Article
- Comparison of ultrafast wave-controlled aliasing in parallel imaging (CAIPI) magnetization-prepared rapid acquisition gradient echo (MP-RAGE) and standard MP-RAGE in non-sedated children: initial clinical experience.Pediatric radiology · 2021Article
- A particle swarm optimization improved BP neural network intelligent model for electrocardiogram classification.BMC medical informatics and decision making · 2021Article
Corrections and comments
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6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While high-resolution proton density-weighted magnetic resonance imaging (MRI) of intracranial vessel walls is significant for a precise diagnosis of intracranial artery disease, its long acquisition time is a clinical burden. Compressed sensing MRI is a prospective technology with acceleration factors that could potentially reduce the scan time. However, high acceleration factors result in degraded image quality. Although recent advances in deep-learning-based image restoration algorithms can alleviate this problem, clinical image pairs used in deep learning training typically do not align pixel-wise. Therefore, in this study, two different deep-learning-based denoising algorithms-self-supervised learning and unsupervised learning-are proposed; these algorithms are applicable to clinical datasets that are not aligned pixel-wise. The two approaches are compared quantitatively and qualitatively. Both methods produced promising results in terms of image denoising and visual grading. While the image noise and signal-to-noise ratio of self-supervised learning were superior to those of unsupervised learning, unsupervised learning was preferable over self-supervised learning in terms of radiomic feature reproducibility.
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