Evidence map›Paper›PMID 39967806›Full record

ArticleFrontiers in neuroscience2025

Stochastic variational inference improves quantification of multiple timepoint arterial spin labelling perfusion MRI.

Thomas F Kirk, Georgia G Kenyon, Martin S Craig, Michael A Chappell

Abstract read
In one paragraph

Article in Frontiers in neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Thomas F KirkQuantified Imaging Limited, London, United Kingdom.
Georgia G KenyonSir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom.
Martin S CraigQuantified Imaging Limited, London, United Kingdom.
Michael A ChappellQuantified Imaging Limited, London, United Kingdom.

Funding

MAPPING THE HUMAN CONNECTOME DURING TYPICAL AGINGU01AG052564 · NIA · WASHINGTON UNIVERSITY · PI SALAT, DAVID H, TERPSTRA, MELISSA J · 2016 to 2020
$18.9M
Mapping the Human Connectome During Typical DevelopmentU01MH109589 · NIMH · WASHINGTON UNIVERSITY · PI BARCH, DEANNA, BOOKHEIMER, SUSAN Y · 2016 to 2019
$17.1M
NIA NIH HHS U01 AG052564NIMH NIH HHS U01 MH109589
6 · The paper itself

Abstract

Multiple-timepoint arterial spin labelling MRI is a non-invasive imaging technique that permits measurement of both cerebral blood flow and arterial transit time, the latter of which is an emerging biomarker of interest for cerebrovascular health. Quantification of arterial spin labelling data is challenging due to the low signal to noise ratio and non-linear tracer kinetics of this technique. In this work, we introduce a new quantification method called SSVB that addresses limitations in existing methods and demonstrate its performance using simulations and acquisition data. Simulations showed that the method is more accurate, particularly for estimating arterial transit time, and more robust to noise than existing techniques. On high spatial resolution data acquired at 3 T, the method produced less noisy parameter maps than the comparator method and captured greater variation in arterial transit time on a cross-sectional cohort.

Indexed as

arterial spin label (ASL) MRIarterial transit time (ATT)cerebral blood flow (CBF)perfusionquantification

Identifiers

PMID39967806
PMCPMC11832661

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.