Evidence map›Paper›PMID 39816441›Full record

ArticleJournal of vascular surgery cases and innovative techniques2025

Feasibility of measuring magnetic resonance elastography-derived stiffness in human thoracic aorta and aortic dissection phantoms.

Adnan Hirad, Faisal S Fakhouri, Brian Raterman, Ronald Lakony, Maxwell Wang, Dakota Gonring, Baqir Kedwai, Arunark Kolipaka, Doran Mix

Abstract read
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In one paragraph

Article in Journal of vascular surgery cases and innovative techniques, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

9 authors.

Adnan HiradDepartment of Surgery, University of Rochester School of Medicine, Rochester, NY.
Faisal S FakhouriDepartment of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh, Kingdom of Saudi Arabia.
Brian RatermanDepartment of Radiology, The Ohio State University, Wexner Medical Center, Columbus, OH.
Ronald LakonyDepartment of Surgery, University of Rochester School of Medicine, Rochester, NY.
Maxwell WangDivision of Vascular Surgery, Loma Linda University Health Medical Center, Loma Linda, CA.
Dakota GonringDepartment of Surgery, University of Rochester School of Medicine, Rochester, NY.
Baqir KedwaiDepartment of Surgery, University of Rochester School of Medicine, Rochester, NY.
Arunark KolipakaDepartment of Radiology, The Ohio State University, Wexner Medical Center, Columbus, OH.
Doran MixDepartment of Surgery, University of Rochester School of Medicine, Rochester, NY.

Funding

Abdominal Aortic Aneurysm Ultrasound Elasticity ImagingR21EB018432 · NIBIB · UNIVERSITY OF ROCHESTER · PI RICHARDS, MICHAEL SCOTT, STONER, MICHAEL · 2015 to 2016
$429k
NIBIB NIH HHS R21 EB018432
6 · The paper itself

Abstract

Type B aortic dissection (TBAD) represents a serious medical emergency with up to a 50% associated 5-year mortality caused by thoracic aorta, dissection-associated aneurysmal (DAA) degeneration, and rupture. Unfortunately, conventional size-related diagnostic methods cannot distinguish high-risk DAAs that benefit from surgical intervention from stable DAAs. Our goal is to use DAA stiffness measured with magnetic resonance elastography (MRE) as a biomarker to distinguish high-risk DAAs from stable DAAs. This is a feasibility study using MRE to (1) fabricate human-like geometries TBAD phantoms with different stiffnesses, (2) measure stiffness in TBAD phantoms with rheometry, and (3) demonstrate the first successful application of MRE to the thoracic aorta of a human volunteer. AD phantoms with heterogenous wall stiffness demonstrated the correlation between MRE-derived stiffness and rheometric measured stiffness. A pilot scan was performed in a healthy volunteer to test the technique's feasibility in the thoracic aorta.

Indexed as

Aortic biomechanicsAortic dissectionMagnetic resonance elastographyThoracic aortic stiffness

Identifiers

PMID39816441
PMCPMC11732680

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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.