ArticleNeuroimage. Reports2023
Deep Learning Based Detection of Enlarged Perivascular Spaces on Brain MRI.
Article in Neuroimage. Reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 29 citations in OpenAlex.
- Intensive versus standard blood pressure control and overall brain small vessel disease burden: a post-hoc analysis of the SPRINT randomized clinical trial.EClinicalMedicine · 2026Article
- Association of Lacunes with Risk Factors, Cognition, and Atrophy: The Multi-Ethnic Study of Atherosclerosis (MESA).medRxiv : the preprint server for health sciences · 2026Article
- A comprehensive framework for automated segmentation of perivascular spaces in brain MRI with the nnU-Net.Neuroradiology · 2026Article
- Unravelling the genetic architecture of cerebral small vessel disease in the context of stroke.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2026Review
- Evaluation of an in vivo biomarker of arteriolosclerosis (ARTS) and its associations with cognition and multimodal ATN(V) biomarkers in a cardiometabolic-risk enriched community cohort.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Deep Learning Auto-segmentation of Diffuse Midline Glioma on Multimodal Magnetic Resonance Images.Journal of imaging informatics in medicine · 2026Article
- "Mind the Gap"-enlarged perivascular spaces as a potential magnetic resonance imaging biomarker of impaired glymphatic clearance in brain disorders.Frontiers in cellular neuroscience · 2026Review
- Evaluation of an in vivo biomarker of arteriolosclerosis (ARTS) and its associations with cognition and multimodal ATN(V) biomarkers in a cardiometabolic-risk enriched community cohort.medRxiv : the preprint server for health sciences · 2025Article
- DRIPS: Domain Randomisation for Image-based Perivascular spaces Segmentation.medRxiv : the preprint server for health sciences · 2025Article
- An exploratory study of the diffusion tensor imaging analysis along perivascular spaces (DTI-ALPS) index combined with quantitative analysis of choroid plexus volume and perivascular spaces in different cognitive stages of cerebral small vessel disease.Quantitative imaging in medicine and surgery · 2025Article
- Assessing the Global Impact of Brain Small Vessel Disease on Cognition: The Multi-Ethnic Study of Atherosclerosis.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Article
- Deep Cascade of Convolutional Neural Networks for Quantification of Enlarged Perivascular Spaces in the Basal Ganglia in Magnetic Resonance Imaging.Diagnostics (Basel, Switzerland) · 2024Article
- Cerebral perivascular spaces as predictors of dementia risk and accelerated brain atrophy.medRxiv : the preprint server for health sciences · 2024Article
- Quantitative assessment of enlarged perivascular spaces via deep-learning in community-based older adults reveals independent associations with vascular neuropathologies, vascular risk factors and cognition.Brain communications · 2024Article
- Deep-learning-based segmentation of perivascular spaces on T2-Weighted 3T magnetic resonance images.Frontiers in aging neuroscience · 2024Article
- Deep Learning Network with Spatial Attention Module for Detecting Acute Bilirubin Encephalopathy in Newborns Based on Multimodal MRI.Diagnostics (Basel, Switzerland) · 2023Article
- Assessment of Risk Factors and Clinical Importance of Enlarged Perivascular Spaces by Whole-Brain Investigation in the Multi-Ethnic Study of Atherosclerosis.JAMA network open · 2023Article
- Postmortem Brain Imaging in Alzheimer's Disease and Related Dementias: The South Texas Alzheimer's Disease Research Center Repository.Journal of Alzheimer's disease : JAD · 2023Article
- A critical guide to the automated quantification of perivascular spaces in magnetic resonance imaging.Frontiers in neuroscience · 2022Review
Corrections and comments
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Authors and funding
15 authors at 7 institutions in 2 countries.
Funding
Abstract
Deep learning has been demonstrated effective in many neuroimaging applications. However, in many scenarios, the number of imaging sequences capturing information related to small vessel disease lesions is insufficient to support data-driven techniques. Additionally, cohort-based studies may not always have the optimal or essential imaging sequences for accurate lesion detection. Therefore, it is necessary to determine which imaging sequences are crucial for precise detection. This study introduces a deep learning framework to detect enlarged perivascular spaces (ePVS) and aims to find the optimal combination of MRI sequences for deep learning-based quantification. We implemented an effective lightweight U-Net adapted for ePVS detection and comprehensively investigated different combinations of information from SWI, FLAIR, T1-weighted (T1w), and T2-weighted (T2w) MRI sequences. The experimental results showed that T2w MRI is the most important for accurate ePVS detection, and the incorporation of SWI, FLAIR and T1w MRI in the deep neural network had minor improvements in accuracy and resulted in the highest sensitivity and precision (sensitivity =0.82, precision =0.83). The proposed method achieved comparable accuracy at a minimal time cost compared to manual reading. The proposed automated pipeline enables robust and time-efficient readings of ePVS from MR scans and demonstrates the importance of T2w MRI for ePVS detection and the potential benefits of using multimodal images. Furthermore, the model provides whole-brain maps of ePVS, enabling a better understanding of their clinical correlates compared to the clinical rating methods within only a couple of brain regions.
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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.