ArticleIEEE access : practical innovations, open solutions2019
Enhancement of Perivascular Spaces Using Densely Connected Deep Convolutional Neural Network.
Article in IEEE access : practical innovations, open solutions, 2019. 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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- Article
- Evaluation of non-motor symptoms in Parkinson's disease using multiparametric MRI with the multiplex sequence.Frontiers in aging neuroscience · 2025Article
- AI for BPH Surgical Decision-Making: Cost Effectiveness and Outcomes.Current urology reports · 2024Review
- Deep Cascade of Convolutional Neural Networks for Quantification of Enlarged Perivascular Spaces in the Basal Ganglia in Magnetic Resonance Imaging.Diagnostics (Basel, Switzerland) · 2024Article
- Application of artificial intelligence-based magnetic resonance imaging in diagnosis of cerebral small vessel disease.CNS neuroscience & therapeutics · 2024Review
- Deep Learning Based Detection of Enlarged Perivascular Spaces on Brain MRI.Neuroimage. Reports · 2023Article
- Imaging perivascular space structure and function using brain MRI.NeuroImage · 2022Review
- Automated grading of enlarged perivascular spaces in clinical imaging data of an acute stroke cohort using an interpretable, 3D deep learning framework.Scientific reports · 2022Article
- A critical guide to the automated quantification of perivascular spaces in magnetic resonance imaging.Frontiers in neuroscience · 2022Review
- Perivascular Space Imaging at Ultrahigh Field MR Imaging.Magnetic resonance imaging clinics of North America · 2021Review
- 3D Segmentation of Perivascular Spaces on T1-Weighted 3 Tesla MR Images With a Convolutional Autoencoder and a U-Shaped Neural Network.Frontiers in neuroinformatics · 2021Article
- Image processing approaches to enhance perivascular space visibility and quantification using MRI.Scientific reports · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Perivascular spaces (PVS) in the human brain are related to various brain diseases. However, it is difficult to quantify them due to their thin and blurry appearance. In this paper, we introduce a deep-learning-based method, which can enhance a magnetic resonance (MR) image to better visualize the PVS. To accurately predict the enhanced image, we propose a very deep 3D convolutional neural network that contains densely connected networks with skip connections. The proposed networks can utilize rich contextual information derived from low-level to high-level features and effectively alleviate the gradient vanishing problem caused by the deep layers. The proposed method is evaluated on 17 7T MR images by a twofold cross-validation. The experiments show that our proposed network is much more effective to enhance the PVS than the previous PVS enhancement methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.