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MANA - Multi scale adaptive normalized averaging
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9267-2191
Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Faculty of Health Sciences.
2011 (English)In: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, IEEE conference proceedings, 2011, p. 361-364Conference paper, Published paper (Refereed)
Abstract [en]

It is possible to correct intensity inhomogeneity in fat–water Magnetic Resonance Imaging (MRI) by estimating a bias field based on the observed intensities of voxels classified as the pure adipose tissue. The same procedure can also be used to quantify fat volume and its distribution which opens up for new medical applications. The bias field estimation method has to be robust since pure fat voxels are irregularly located and the density varies greatly within and between image volumes. This paper introduces Multi scale Adaptive Normalized Average (MANA) that solves this problem bybasing the estimate on a scale space of weighted averages. By usingthe local certainty of the data MANA preserves details where the local data certainty is high and provides realistic values in sparse areas.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2011. p. 361-364
Series
International Symposium on Biomedical Imaging. Proceedings, ISSN 1945-7928
National Category
Medical Laboratory and Measurements Technologies Computer Vision and Robotics (Autonomous Systems) Radiology, Nuclear Medicine and Medical Imaging
Identifiers
URN: urn:nbn:se:liu:diva-67848DOI: 10.1109/ISBI.2011.5872424ISI: 000298849400083ISBN: 978-1-4244-4128-0 (print)OAI: oai:DiVA.org:liu-67848DiVA, id: diva2:413635
Conference
IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Chicago, IL, USA, March 30 2011-April 2 2011
Available from: 2011-04-29 Created: 2011-04-29 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

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Romu, ThobiasBorga, MagnusDahlqvist Leinhard, Olof

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Romu, ThobiasBorga, MagnusDahlqvist Leinhard, Olof
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