Evidence map›Paper›PMID 24123229›Full record

ReviewNMR in biomedicine2013

Quantitative proton MR techniques for measuring fat.

H H Hu, H E Kan

Open access · greenAbstract readReview
In one paragraph

Review in NMR in biomedicine, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers.

0numbers the graph read from it
0cells of the map it votes in
63citing papers in PubMed
11.7field-weighted citation impact, top 1% 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

63 citing papers in PubMed, 123 citations in OpenAlex.

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3 more citing papers are in PubMed but not listed here.

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

2 authors at 2 institutions in 2 countries.

H H HuDepartment of Radiology, Children's Hospital Los Angeles, University of Southern California, Los Angeles, CA, USA.
H E Kan
Leiden University Medical Center · NLUniversity of Southern California · US

Funding

EXPANDING THE APPLICATION OF MRI FOR BODY FAT STUDIES IN HUMANSK25DK087931 · NIDDK · UNIVERSITY OF SOUTHERN CALIFORNIA · PI HU, HOUCHUN H. · 2010 to 2013
$543k
NIDDK NIH HHS K25 DK087931NIDDK NIH HHS K25DK087931
6 · The paper itself

Abstract

Accurate, precise and reliable techniques for the quantification of body and organ fat distributions are important tools in physiology research. They are critically needed in studies of obesity and diseases involving excess fat accumulation. Proton MR methods address this need by providing an array of relaxometry-based (T1, T2) and chemical shift-based approaches. These techniques can generate informative visualizations of regional and whole-body fat distributions, yield measurements of fat volumes within specific body depots and quantify fat accumulation in abdominal organs and muscles. MR methods are commonly used to investigate the role of fat in nutrition and metabolism, to measure the efficacy of short- and long-term dietary and exercise interventions, to study the implications of fat in organ steatosis and muscular dystrophies and to elucidate pathophysiological mechanisms in the context of obesity and its comorbidities. The purpose of this review is to provide a summary of mainstream MR strategies for fat quantification. The article succinctly describes the principles that differentiate water and fat proton signals, summarizes the advantages and limitations of various techniques and offers a few illustrative examples. The article also highlights recent efforts in the MR of brown adipose tissue and concludes by briefly discussing some future research directions.

Indexed as

Magnetic Resonance SpectroscopyProtonsAdipose Tissue, BrownAdiposityAnimalsHumansProtonschemical shiftectopic fatfat quantificationmuscleobesityspectroscopywater-fat MRIwhite and brown adipose tissue

Identifiers

PMID24123229
PMCPMC4001818
OpenAlexW1777574597

What OpenQuestion holds

Textmetadata
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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.