Evidence map›Paper›PMID 34181130›Full record

ArticleAnnals of biomedical engineering2021

Experimental Validation of Enhanced Magnetic Resonance Imaging (EMRI) Using Particle Image Velocimetry (PIV).

Giacomo Annio, Ryo Torii, Andrea Ducci, Vivek Muthurangu, Victor Tsang, Gaetano Burriesci

Abstract readValidation Study
PubMed Publisher
In one paragraph

Article in Annals of biomedical engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 32% 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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. 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

6 authors at 4 institutions in 2 countries.

Giacomo AnnioDepartment of Medical Physics and Biomedical Engineering, University College London, London, UK. anniogiacomo@gmail.com.ORCID http://orcid.org/0000-0002-0646-635X
Ryo ToriiDepartment of Mechanical Engineering, University College London, London, UK. r.torii@ucl.ac.uk.
Andrea DucciDepartment of Mechanical Engineering, University College London, London, UK.
Vivek MuthuranguCentre for Cardiovascular Imaging and Physics, University College London, London, UK.
Victor TsangCardiothoracic Surgery Unit, Great Ormond Street Hospital, London, UK.
Gaetano BurriesciDepartment of Mechanical Engineering, University College London, London, UK. g.burriesci@ucl.ac.uk.
University College London · GBGreat Ormond Street Hospital · GBRi.MED · ITThe London College · GB

Funding

Engineering and Physical Sciences Research Council EP/L016478/1
6 · The paper itself

Abstract

Flow-sensitive four-dimensional Cardiovascular Magnetic Resonance Imaging (4D Flow CMR) has increasingly been utilised to characterise patients' blood flow, in association with patiens' state of health and disease, even though spatial and temporal resolutions still constitute a limit. Computational fluid dynamics (CFD) is a powerful tool that could expand these information and, if integrated with experimentally-obtained velocity fields, would enable to derive a large variety of the flow descriptors of interest. However, the accuracy of the flow parameters is highly influenced by the quality of the input data such as the anatomical model and boundary conditions typically derived from medical images including 4D Flow CMR. We previously proposed a novel approach in which 4D Flow CMR and CFD velocity fields are integrated to obtain an Enhanced 4D Flow CMR (EMRI), allowing to overcome the spatial-resolution limitation of 4D Flow CMR, and enable an accurate quantification of flow. In this paper, the proposed approach is validated in a U bend channel, an idealised model of the human aortic arch. The flow patterns were studied with 4D Flow CMR, CFD and EMRI, and compared with high resolution 2D PIV experiments obtained in pulsatile conditions. The main strengths and limitations of 4D Flow CMR and CFD were illustrated by exploiting the accuracy of PIV by comparing against PIV velocity fields. EMRI flow patterns showed a better qualitative and quantitative agreement with PIV results than the other techniques. EMRI enables to overcome the experimental limitations of MRI-based velocity measurements and the modelling simplifications of CFD, allowing an accurate prediction of complex flow patterns observed experimentally, while satisfying mass and momentum balance equations.

Indexed as

Models, CardiovascularAortaHumansMagnetic Resonance ImagingRheology4D Flow CMRCardiovascular flowCFDPIVU-bend

Identifiers

PMID34181130
OpenAlexW3173213084

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.