Evidence map›Paper›PMID 34105167›Full record

ArticleMedical physics2021

Multisite multivendor validation of a quantitative MRI and CT compatible fat phantom.

Ruiyang Zhao, Diego Hernando, David T Harris, Louis A Hinshaw, Ke Li, Lakshmi Ananthakrishnan, Mustafa R Bashir, Xinhui Duan, Mounes Aliyari Ghasabeh, Ihab R Kamel and 9 more

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Medical physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
2.1field-weighted citation impact, top 13% 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

11 citing papers in PubMed, 22 citations in OpenAlex.

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  11. RSC advances · 2022
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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

19 authors at 6 institutions in 1 country.

Ruiyang ZhaoDepartment of Radiology, University of Wisconsin-Madison, Madison, WI, USA.
Diego HernandoDepartment of Radiology, University of Wisconsin-Madison, Madison, WI, USA.
David T HarrisDepartment of Radiology, University of Wisconsin-Madison, Madison, WI, USA.
Louis A HinshawDepartment of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA.
Ke LiDepartment of Radiology, University of Wisconsin-Madison, Madison, WI, USA.
Lakshmi AnanthakrishnanDepartment of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Mustafa R BashirDepartment of Radiology, Duke University, Durham, NC, USA.
Xinhui DuanDepartment of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Mounes Aliyari GhasabehDepartment of Radiology, Johns Hopkins University, Baltimore, MD, USA.
Ihab R KamelDepartment of Radiology, Johns Hopkins University, Baltimore, MD, USA.
Carolyn LowryDepartment of Radiology, Duke University, Durham, NC, USA.
Mahadevappa MaheshDepartment of Radiology, Johns Hopkins University, Baltimore, MD, USA.
Daniele MarinDepartment of Radiology, Duke University, Durham, NC, USA.
Jessica MillerDepartment of Human Oncology, University of Wisconsin-Madison, Madison, WI, USA.
Perry J PickhardtDepartment of Radiology, University of Wisconsin-Madison, Madison, WI, USA.
Jean ShafferDepartment of Radiology, Duke University, Durham, NC, USA.
Takeshi YokooDepartment of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Jean H BrittainCalimetrix, LLC, Madison, WI, USA.
Scott B ReederDepartment of Radiology, University of Wisconsin-Madison, Madison, WI, USA.
University of Wisconsin–Madison · USDuke University · USJohns Hopkins University · USThe University of Texas Southwestern Medical Center · USNeuroMetrix (United States) · USResonance Research (United States) · US

Funding

Technical Validation of MRI Biomarkers of Liver FatR01DK088925 · NIDDK · UNIVERSITY OF WISCONSIN-MADISON · PI REEDER, SCOTT B., SIRLIN, CLAUDE B · 2010 to 2021
$7.1M
Confounder-Corrected Quantitative MRI Biomarkers of HepaticR01DK100651 · NIDDK · UNIVERSITY OF WISCONSIN-MADISON · PI REEDER, SCOTT B. · 2014 to 2017
$2.7M
Quantitative Magnetic Resonance Imaging PhantomsR44EB025729 · NIBIB · CALIMETRIX, LLC · PI BRITTAIN, JEAN H., HERNANDO, DIEGO · 2019 to 2020
$1.5M
Technical Development of Quantitative Biomarkers of Fatty Liver Disease with MRIR01DK083380 · NIDDK · UNIVERSITY OF WISCONSIN-MADISON · PI REEDER, SCOTT B. · 2010 to 2013
$1.2M
Evolution of Quantitative MRI Biomarkers of Metabolic Syndrome During PubertyK24DK102595 · NIDDK · UNIVERSITY OF WISCONSIN-MADISON · PI REEDER, SCOTT B. · 2014 to 2018
$927k
Quantitative Magnetic Resonance Imaging PhantomsR41EB025729 · NIBIB · CALIMETRIX, LLC · PI HERNANDO, DIEGO · 2018 to 2018
$239k
NIBIB NIH HHS R41 EB025729NIBIB NIH HHS R44 EB025729NIDDK NIH HHS K24 DK102595NIDDK NIH HHS R01 DK083380NIDDK NIH HHS R01 DK088925NIDDK NIH HHS R01 DK100651NIH HHS K24 DK102595NIH HHS R01 DK083380NIH HHS R01 DK088925NIH HHS R01 DK100651NIH HHS R41 EB025729NIH HHS R44 EB025729
6 · The paper itself

Abstract

purposeChemical shift-encoded magnetic resonance imaging enables accurate quantification of liver fat content though estimation of proton density fat-fraction (PDFF). Computed tomography (CT) is capable of quantifying fat, based on decreased attenuation with increased fat concentration. Current quantitative fat phantoms do not accurately mimic the CT number of human liver. The purpose of this work was to develop and validate an optimized phantom that simultaneously mimics the MRI and CT signals of fatty liver.

methodsAn agar-based phantom containing 12 vials doped with iodinated contrast, and with a granular range of fat fractions was designed and constructed within a novel CT and MR compatible spherical housing design. A four-site, three-vendor validation study was performed. MRI (1.5T and 3T) and CT images were obtained using each vendor's PDFF and CT reconstruction, respectively. An ROI centered in each vial was placed to measure MRI-PDFF (%) and CT number (HU). Mixed-effects model, linear regression, and Bland-Altman analysis were used for statistical analysis.

resultsMRI-PDFF agreed closely with nominal PDFF values across both field strengths and all MRI vendors. A linear relationship (slope = -0.54 ± 0.01%/HU, intercept = 37.15 ± 0.03%) with an R

conclusionsThe proposed fat phantom successfully mimicked quantitative liver signal for both MRI and CT. The proposed fat phantom in this study may facilitate broader application and harmonization of liver fat quantification techniques using MRI and CT across institutions, vendors and imaging platforms.

Indexed as

Magnetic Resonance ImagingTomography, X-Ray ComputedHumansLiverMagnetic Resonance SpectroscopyPhantoms, ImagingReproducibility of Resultscomputed tomographyfatlivermagnetic resonance imagingphantom

Identifiers

PMID34105167
PMCPMC8859818
OpenAlexW3166116518

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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.