liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Quantifying Abdominal Adipose Tissue and Thigh Muscle Volume and Hepatic Proton Density Fat Fraction: Repeatability and Accuracy of an MR Imaging–based, Semiautomated Analysis Method
Department of Radiology, University of California, San Diego, CA, USA.
Department of Radiology, University of California, San Diego, CA, USA.
Department of Radiology, University of California, San Diego, CA, USA.
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9267-2191
Show others and affiliations
2017 (English)In: Radiology, ISSN 0033-8419, E-ISSN 1527-1315, Vol. 283, no 2, 438-449 p.Article in journal (Refereed) Published
Abstract [en]

Purpose

The purpose of this study was to determine the repeatability and accuracy of an   commercially available (Advanced MR Analytics [AMRA®]; Linköping, Sweden) magnetic resonance imaging (MRI)-based, semi-automated method to quantify abdominal adipose tissue and thigh muscle volume as well as hepatic proton density fat fraction (PDFF)

Materials and Methods

This prospective study was approved by an institutional review board (IRB) and was Health Insurance Portability and Accountability Act (HIPAA) compliant. All subjects provided written informed consent. Inclusion criteria were age ≥ 18 years, and willingness to participate. Exclusion criteria were contraindication to MRI. Three-dimensional, T1-weighted, dual-echo body-coil images were acquired from base of skull to knees at 3T, twice before and once after taking subjects off the scanner table (total of three acquisitions). Source images were reconstructed offline to generate water, and calibrated fat images where pure adipose tissue has unit value and absence of adipose tissue has zero value. Abdominal adipose tissues and thigh muscles were segmented, and their volumes estimated using AMRA  a semi-automated analysis method and, as a reference standard, manually. Hepatic PDFF was estimated using a confounder-corrected chemical-shift encoded MRI method with hybrid complex-magnitude reconstruction., and, as a reference standard, with magnetic resonance spectroscopy (MRS). Tissue volume and hepatic PDFF intra- and inter-examination repeatability was assessed by intraclass correlation (ICC) and coefficient of variation (CV) analysis. Tissue volume and hepatic PDFF accuracies were assessed by linear regression using their respective reference standards.

Results

Twenty adult subjects were enrolled (18 female, age range 25 - 76 yrs, body mass index range 19.3 to 43.9 kg/m2). Adipose and thigh muscle tissue volumes estimated using the semi-automated analysis method had intra-and inter-examination ICCs between 0.996 and 0.998, and CVs between 1.5 and 3.6%. For hepatic MRI PDFF, intra- and inter-examination ICCs were ≥ 0.994 and CVs, ≤ 7.3%. Agreement between semi-automated and manual volume estimates, and between MRI and MRS hepatic PDFF estimates, was high, with regression slopes and intercepts not significantly different from the identity line (all p’s > 0.05), and R2’s between 0.744 and 0.994.

Conclusions

This MRI-based, semi-automated method provides high repeatability, and high accuracy for estimating abdominal adipose tissue and thigh muscle volumes, and hepatic PDFF.

Place, publisher, year, edition, pages
Radiological Society of North America, Inc. , 2017. Vol. 283, no 2, 438-449 p.
Keyword [en]
Liver, proton density fat fraction, PDFF, repeatability, accuracy, semi-automated, SCAT, VAT.
National Category
Radiology, Nuclear Medicine and Medical Imaging Medical Image Processing
Identifiers
URN: urn:nbn:se:liu:diva-133623DOI: 10.1148/radiol.2017160606ISI: 000401897800012PubMedID: 28278002OAI: oai:DiVA.org:liu-133623DiVA: diva2:1061387
Note

Funding agencies: Pfizer; National Institutes of Health [R01 DK088925]

Available from: 2017-01-02 Created: 2017-01-02 Last updated: 2017-08-07

Open Access in DiVA

No full text

Other links

Publisher's full textPubMed

Search in DiVA

By author/editor
Borga, MagnusDahlqvist Leinhard, OlofRomu, Thobias
By organisation
Center for Medical Image Science and Visualization (CMIV)Medical InformaticsThe Institute of TechnologyDepartment of Radiation PhysicsDivision of Radiological SciencesFaculty of Medicine and Health SciencesFaculty of Science & Engineering
In the same journal
Radiology
Radiology, Nuclear Medicine and Medical ImagingMedical Image Processing

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 268 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf