Evidence map›Paper›PMID 34950643›Full record

ArticleFrontiers in bioengineering and biotechnology2021

Hojin Ha, Hyung Kyu Huh, Kyung Jin Park, Petter Dyverfeldt, Tino Ebbers, Dae-Hee Kim, Dong Hyun Yang

Open access · goldAbstract read
In one paragraph

Article in Frontiers in bioengineering and biotechnology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.9field-weighted citation impact, top 22% 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

3 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Article
  3. 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

7 authors at 4 institutions in 2 countries.

Hojin HaDepartment of Mechanical and Biomedical Engineering, Kangwon National University, Chuncheon, South Korea.
Hyung Kyu HuhDaegu-Gyeongbuk Medical Innovation Foundation, Medical Device Development Center, Daegu, South Korea.
Kyung Jin ParkDepartment of Electrical and Electronic Engineering, Yonsei Univeristy, Seoul, South Korea.
Petter DyverfeldtDepartment of Health, Medicine and Caring Science, Linköping University, Linköping, Sweden.
Tino EbbersDepartment of Health, Medicine and Caring Science, Linköping University, Linköping, Sweden.
Dae-Hee KimDepartment of Cardiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Dong Hyun YangDepartment of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
University of Ulsan · KRLinköping University · SEDaegu-Gyeongbuk Medical Innovation Foundation · KRKangwon National University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Imaging hemodynamics play an important role in the diagnosis of abnormal blood flow due to vascular and valvular diseases as well as in monitoring the recovery of normal blood flow after surgical or interventional treatment. Recently, characterization of turbulent blood flow using 4D flow magnetic resonance imaging (MRI) has been demonstrated by utilizing the changes in signal magnitude depending on intravoxel spin distribution. The imaging sequence was extended with a six-directional icosahedral (ICOSA6) flow-encoding to characterize all elements of the Reynolds stress tensor (RST) in turbulent blood flow. In the present study, we aimed to demonstrate the feasibility of full RST analysis using ICOSA6 4D flow MRI under physiological conditions. First, the turbulence analysis was performed through

Indexed as

hemodynamicsmagnetic resonace imagingturbulence measurementturbulence productionturbulent kinetic energy

Identifiers

PMID34950643
PMCPMC8691458
OpenAlexW4200251048

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.