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

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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
Characterization of Brown Adipose Tissue by water-fat separated Magnetic Resonance Imaging
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
Göteborgs universitet.
Show others and affiliations
2015 (English)In: Journal of Magnetic Resonance Imaging, ISSN 1053-1807, E-ISSN 1522-2586, Vol. 42, no 6, p. 1639-1645Article in journal (Refereed) Published
Abstract [en]

Purpose: To evaluate the possibility of quantifying brown adipose tissue (BAT) volume and fat concentration with a high resolution, long TE, dual-echo Dixon imaging protocol.

Materials and methods: A 0.42 mm isotropic resolution water-fat separated MRI protocol was implemented by utilizing the second opposite-phase echo and third in-phase echo. Fat images were calibrated with regard to the intensity of nearby white adipose tissue (WAT) to form relative fat content (RFC) images. To evaluate the ability to measure BAT volume and RFC contrast dynamics, rats were divided into two groups that were kept at 4° or 22° C for five days. The rats were then scanned in a 70 cm bore 3.0 T MRI scanner and a human dual energy CT. Interscapular, paraaortal and perirenal BAT (i/pa/pr-BAT) depots as well as WAT and muscle were segmented in the MRI and CT images. Biopsies were collected from the identified BAT depots.

Results: The biopsies confirmed that the three depots identified with the RFC images consisted of BAT. There was a significant linear correlation (p <0.001) between the measured RFC and the Hounsfield units from DECT. Significantly lower iBAT RFC (p = 0.0064) and significantly larger iBAT and prBAT volumes (p=0.0017) were observed in the cold stimulated rats.

Conclusions: The calibrated Dixon images with RFC scaling can depict BAT and be used to measure differences in volume, and fat concentration, induced by cold stimulation. The high correlation between RFC and HU suggests that the fat concentration is the main RFC image contrast mechanism.

Place, publisher, year, edition, pages
John Wiley & Sons, 2015. Vol. 42, no 6, p. 1639-1645
Keywords [en]
Brown Adipose Tissue; BAT; Fat Water MRI
National Category
Medical Image Processing Radiology, Nuclear Medicine and Medical Imaging
Identifiers
URN: urn:nbn:se:liu:diva-116931DOI: 10.1002/jmri.24931ISI: 000368258100020OAI: oai:DiVA.org:liu-116931DiVA, id: diva2:801651
Funder
Knowledge Foundation, 2011.0059Available from: 2015-04-09 Created: 2015-04-09 Last updated: 2018-02-22
In thesis
1. Fat-Referenced MRI: Quanitaive MRI for Tissue Characterizaion and Volume Measurement
Open this publication in new window or tab >>Fat-Referenced MRI: Quanitaive MRI for Tissue Characterizaion and Volume Measurement
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The amount and distribution of adipose and lean tissues has been shown to be predictive of mortality and morbidity in metabolic disease. Traditionally these risks are assessed by anthropometric measurements based on weight, length, girths or the body mass index (BMI). These measurements are predictive of risks on a population level, where a too low or a too high BMI indicates an increased risk of both mortality and morbidity. However, today a large part of the world’s population belongs to a group with an elevated risk according to BMI, many of which will live long and healthy lives. Thus, better instruments are needed to properly direct health-care resources to those who need it the most.

Medical imaging method can go beyond anthropometrics. Tomographic modalities, such as magnetic resonance imaging (MRI), can measure how we have stored fat in and around organs. These measurements can eventually lead to better individual risk predictions. For instance, a tendency to store fat as visceral adipose tissue (VAT) is associated with an increased risk of diabetes type 2, cardio-vascular disease, liver disease and certain types of cancer. Furthermore, liver fat is associated with liver disease, diabetes type 2. Brown adipose tissue (BAT), is another emerging component of body-composition analysis. While the normal white adipose tissue stores fat, BAT burns energy to produce heat. This unique property makes BAT highly interesting, from a metabolic point of view.

Magnetic resonance imaging can both accurately and safely measure internal adipose tissue compartments, and the fat infiltration of organs. Which is why MRI is often considered the reference method for non-invasive body-composition analysis. The two major challenges of MRI based body-composition analysis are, the between-scanner reproducibility and a cost-effective analysis of the images. This thesis presents a complete implementation of fat-referenced MRI, a technique that produces quantitative images that can increase both inter-scanner and automation of the image analysis.

With MRI, it is possible to construct images where water and fat are separated into paired images. In these images, it easy to depict adipose tissue and lean tissue structures. This thesis takes water-fat MRI one step further, by introducing a quantitative framework called fat-referenced MRI. By calibrating the image using the subjects' own adipose tissue (paper II), the otherwise non-quantitative fat images are made quantitative. In these fat-referenced images it is possible to directly measure the amount of adipose tissue in different compartments. This quantitative property makes image analysis easy and accurate, as lean and adipose tissues can be separated on a sub-voxel level. Fat-referenced MRI further allows the quantification and characterization of BAT.

This thesis work starts by formulating a method to produce water-fat images (paper I) based on two gradient recall images, i.e.\ 2-point Dixon images (2PD). It furthers shows that fat-referenced 2PD images can be corrected for T2*, making the 2PD body-composition measurements comparable with confounder-corrected Dixon measurements (paper III}).

Both the water-fat separation method and fat image calibration are applied to BAT imaging. The methodology is first evaluated in an animal model, where it is shown that it can detect both BAT browning and volume increase following cold acclimatization (paper IV). It is then applied to postmortem imaging, were it is used to locate interscapular BAT in human infants (paper V). Subsequent analysis of biopsies, taken based on the MRI images, showed that the interscapular BAT was of a type not previously believed to exist in humans. In the last study, fat-referenced MRI is applied to BAT imaging of adults. As BAT structures are difficult to locate in many adults, the methodology was also extended with a multi-atlas segmentation methods (paper VI).

In summary, this thesis shows that fat-referenced MRI is a quantitative method that can be used for body-composition analysis. It also shows that fat-referenced MRI can produce quantitative high-resolution images, a necessity for many BAT applications.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2018. p. 85
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1910
Keywords
MRI, water-fat separation, quantitative MRI
National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-145316 (URN)10.3384/diss.diva-145316 (DOI)9789176853511 (ISBN)
Public defence
2018-03-21, Grantisalen, Campus US, Linköping, 09:15 (English)
Opponent
Supervisors
Note

DiVA-länken var felaktig i den tryckta versionen. Den är ändrad i den elektroniska versionen.

Available from: 2018-02-27 Created: 2018-02-22 Last updated: 2018-02-28Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Authority records BETA

Romu, ThobiasElander, LouiseDahlqvist Leinhard, OlofPersson, AndersBorga, Magnus

Search in DiVA

By author/editor
Romu, ThobiasElander, LouiseDahlqvist Leinhard, OlofPersson, AndersBorga, Magnus
By organisation
Medical InformaticsThe Institute of TechnologyCenter for Medical Image Science and Visualization (CMIV)Division of Cell BiologyFaculty of Medicine and Health SciencesDivision of Radiological SciencesFaculty of Health SciencesDepartment of Radiation PhysicsDepartment of Radiology in LinköpingFaculty of Science & Engineering
In the same journal
Journal of Magnetic Resonance Imaging
Medical Image ProcessingRadiology, Nuclear Medicine and Medical Imaging

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 497 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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