ArticleNeuroImage2023
Improving measurement of blood-brain barrier permeability with reduced scan time using deep-learning-derived capillary input function.
Article in NeuroImage, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 6 citations in OpenAlex.
- Beyond the Leak: Advanced MRI Assessment of the Blood-Brain Barrier in Neurodegeneration.Neuro-degenerative diseases · 2026Review
- Optimizing bladder magnetic resonance imaging: accelerating scan time and improving image quality through deep learning.Abdominal radiology (New York) · 2025Article
- Imaging markers of neuroinflammation in aging and Alzheimer's disease and related dementia: A comprehensive review.Neuroscience and biobehavioral reviews · 2025Review
- DCE-Qnet: deep network quantification of dynamic contrast enhanced (DCE) MRI.Magma (New York, N.Y.) · 2024Article
- Article
- Feasibility of measuring blood-brain barrier permeability using ultra-short echo time radial magnetic resonance imaging.Journal of neuroimaging : official journal of the American Society of NeuroimagingArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
purposeIn Dynamic contrast-enhanced MRI (DCE-MRI), Arterial Input Function (AIF) has been shown to be a significant contributor to uncertainty in the estimation of kinetic parameters. This study is to assess the feasibility of using a deep learning network to estimate local Capillary Input Function (CIF) to estimate blood-brain barrier (BBB) permeability, while reducing the required scan time. MATERIALS AND
methodA total of 13 healthy subjects (younger (<40 y/o): 8, older (> 67 y/o): 5) were recruited and underwent 25-min DCE-MRI scans. The 25 min data were retrospectively truncated to 10 min to simulate a reduced scan time of 10 min. A deep learning network was trained to predict the CIF using simulated tissue contrast dynamics with two vascular transport models. The BBB permeability (PS) was measured using 3 methods: (i) C
resultsWhen compared to the reference method(C
conclusionsWe demonstrated the feasibility of estimating the capillary-level input functions using a deep learning network. We also showed that this method can be used to estimate subtle age-related changes in BBB permeability with reduced scan time, without compromising accuracy. Moreover, the trained deep learning network can automatically select CIF, reducing the potential uncertainty resulting from manual user-intervention.
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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.