Evidence map›Paper›PMID 35042385›Full record

ArticleJournal of the Royal Society, Interface2022

A multi-modality approach for enhancing 4D flow magnetic resonance imaging via sparse representation.

Jiacheng Zhang, Melissa C Brindise, Sean M Rothenberger, Michael Markl, Vitaliy L Rayz, Pavlos P Vlachos

Open access · bronzeAbstract read
In one paragraph

Article in Journal of the Royal Society, Interface, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.8field-weighted citation impact, top 35% of its field
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

4 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Article
  3. 4D flow MRI velocity uncertainty quantification.Magnetic resonance in medicine · 2025
    Article
  4. Review
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

6 authors at 2 institutions in 1 country.

Jiacheng ZhangSchool of Mechanical Engineering, Purdue University, West Lafayette, IN 47907 USA.ORCID 0000-0002-9027-6531
Melissa C BrindiseSchool of Mechanical Engineering, Purdue University, West Lafayette, IN 47907 USA.ORCID 0000-0002-2047-1630
Sean M RothenbergerWeldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907 USA.
Michael MarklFeinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA.
Vitaliy L RayzSchool of Mechanical Engineering, Purdue University, West Lafayette, IN 47907 USA.
Pavlos P VlachosSchool of Mechanical Engineering, Purdue University, West Lafayette, IN 47907 USA.ORCID 0000-0002-8040-9257
Purdue University West Lafayette · USUniversity of Illinois Chicago · US

Funding

Risk Assessment of Cerebral Aneurysm Growth with 4D flow MRIR01HL115267 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI ANSARI, SAMEER A, LAWTON, MICHAEL T · 2013 to 2023
$4.2M
Multi-modality 4D Flow MRI data enhancement for quantification of cerebral aneurysms hemodynamicsR21NS106696 · NINDS · PURDUE UNIVERSITY · PI RAYZ, VITALIY L, SCHNELL, SUSANNE · 2018 to 2019
$419k
NHLBI NIH HHS R01 HL115267NINDS NIH HHS R21 NS106696
6 · The paper itself

Abstract

This work evaluates and applies a multi-modality approach to enhance blood flow measurements and haemodynamic analysis with phase-contrast magnetic resonance imaging (4D flow MRI) in cerebral aneurysms (CAs). Using a library of high-resolution velocity fields from patient-specific computational fluid dynamic simulations and

Indexed as

Intracranial AneurysmMagnetic Resonance ImagingBlood Flow VelocityHemodynamicsHumansImaging, Three-DimensionalStress, Mechanical4D flow MRIcerebral aneurysmdata fusionhaemodynamic evaluationsparse representation

Identifiers

PMID35042385
PMCPMC8767185
OpenAlexW4206139065

What OpenQuestion holds

Textmetadata
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.