Evidence map›Paper›PMID 33383331›Full record

ArticleMedical image analysis2021

Estimating cardiomyofiber strain in vivo by solving a computational model.

Luigi E Perotti, Ilya A Verzhbinsky, Kévin Moulin, Tyler E Cork, Michael Loecher, Daniel Balzani, Daniel B Ennis

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Medical image analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Myocardial mesostructure and mesofunction.American journal of physiology. Heart and circulatory physiology · 2022
    Review
  5. Article
  6. Article
  7. Arbitrary Point Tracking with Machine Learning to Measure Cardiac Strains in Tagged MRI.Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH (Conference) · 2021
    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.

Luigi E PerottiDepartment of Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL, USA. Electronic address: Luigi.Perotti@ucf.edu.
Ilya A VerzhbinskyDepartment of Radiology, Stanford University, Stanford, CA, USA; Medical Scientist Training Program, University of California, San Diego, La Jolla, USA.
Kévin MoulinDepartment of Radiology, Stanford University, Stanford, CA, USA.
Tyler E CorkDepartment of Radiology, Stanford University, Stanford, CA, USA; Department of Bioengineering, Stanford University, Stanford, CA, USA.
Michael LoecherDepartment of Radiology, Stanford University, Stanford, CA, USA.
Daniel BalzaniChair of Continuum Mechanics, Ruhr University Bochum, Bochum, Germany.
Daniel B EnnisDepartment of Radiology, Stanford University, Stanford, CA, USA.

Funding

Validating Cardiac MRI Biomarkers and Genotype-Phenotype Correlations for DMDR01HL131975 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ENNIS, DANIEL B · 2016 to 2019
$2.5M
Defining and Measuring Diastolic Myocardial Kinematics and StiffnessK25HL135408 · NHLBI · UNIVERSITY OF CENTRAL FLORIDA · PI PEROTTI, LUIGI EMANUELE · 2017 to 2019
$494k
NHLBI NIH HHS K25 HL135408NHLBI NIH HHS R01 HL131975
6 · The paper itself

Abstract

Since heart contraction results from the electrically activated contraction of millions of cardiomyocytes, a measure of cardiomyocyte shortening mechanistically underlies cardiac contraction. In this work we aim to measure preferential aggregate cardiomyocyte ("myofiber") strains based on Magnetic Resonance Imaging (MRI) data acquired to measure both voxel-wise displacements through systole and myofiber orientation. In order to reduce the effect of experimental noise on the computed myofiber strains, we recast the strains calculation as the solution of a boundary value problem (BVP). This approach does not require a calibrated material model, and consequently is independent of specific myocardial material properties. The solution to this auxiliary BVP is the displacement field corresponding to assigned values of myofiber strains. The actual myofiber strains are then determined by minimizing the difference between computed and measured displacements. The approach is validated using an analytical phantom, for which the ground-truth solution is known. The method is applied to compute myofiber strains using in vivo displacement and myofiber MRI data acquired in a mid-ventricular left ventricle section in N=8 swine subjects. The proposed method shows a more physiological distribution of myofiber strains compared to standard approaches that directly differentiate the displacement field.

Indexed as

Heart VentriclesMyocardial ContractionAnimalsMagnetic Resonance ImagingMyocardiumPhantoms, ImagingSwineAnalytical phantomCardiac kinematicsIn vivo cardiomyofiber strainsModel based image analysis

Identifiers

PMID33383331
PMCPMC7956226

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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.