Evidence map›Paper›PMID 29540762›Full record

ArticleScientific reports2018

A Novel Hierarchical Template Matching Model for Cardiac Motion Estimation.

Jayendra M Bhalodiya, Arnab Palit, Manoj K Tiwari, Sanjay K Prasad, Sunil K Bhudia, Theodoros N Arvanitis, Mark A Williams

Abstract read
In one paragraph

Article in Scientific reports, 2018. 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
–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

2 citing papers in PubMed.

  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

7 authors.

Jayendra M BhalodiyaWarwick Manufacturing Group (WMG), University of Warwick, CV4 7AL, Coventry, United Kingdom. j.bhalodiya@warwick.ac.uk.
Arnab PalitWarwick Manufacturing Group (WMG), University of Warwick, CV4 7AL, Coventry, United Kingdom.
Manoj K TiwariIndian Institute of Technology Kharagpur, Kharagpur, West Bengal, India.
Sanjay K PrasadRoyal Brompton and Harefield NHS Foundation Trust, London, United Kingdom.
Sunil K BhudiaRoyal Brompton and Harefield NHS Foundation Trust, London, United Kingdom.
Theodoros N ArvanitisInstitute of Digital Healthcare, WMG, University of Warwick, Coventry, United Kingdom.ORCID 0000-0001-5473-135X
Mark A WilliamsWarwick Manufacturing Group (WMG), University of Warwick, CV4 7AL, Coventry, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cardiovascular disease diagnosis and prognosis can be improved by measuring patient-specific in-vivo local myocardial strain using Magnetic Resonance Imaging. Local myocardial strain can be determined by tracking the movement of sample muscles points during cardiac cycle using cardiac motion estimation model. The tracking accuracy of the benchmark Free Form Deformation (FFD) model is greatly affected due to its dependency on tunable parameters and regularisation function. Therefore, Hierarchical Template Matching (HTM) model, which is independent of tunable parameters, regularisation function, and image-specific features, is proposed in this article. HTM has dense and uniform points correspondence that provides HTM with the ability to estimate local muscular deformation with a promising accuracy of less than half a millimetre of cardiac wall muscle. As a result, the muscles tracking accuracy has been significantly (p < 0.001) improved (30%) compared to the benchmark model. Such merits of HTM provide reliably calculated clinical measures which can be incorporated into the decision-making process of cardiac disease diagnosis and prognosis.

Indexed as

Models, TheoreticalMotionAlgorithmsCardiovascular DiseasesHeartHumans

Identifiers

PMID29540762
PMCPMC5852007

What OpenQuestion holds

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Registered trials

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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.