Zhang, Y;
Hu, X;
Hu, S;
Scotti, A;
Cai, K;
Wang, J;
Zhou, X;
... Zhang, Z; + view all
(2019)
Non-invasive Imaging Methods for Brown Adipose Tissue Detection and Function Evaluation.
Internal Medicine: Open Access
, 8
(6)
, Article 1000299. 10.4172/2165-8048.1000299.
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Abstract
Brown Adipose Tissue (BAT) has a major role in thermoregulation, producing heat by non-shivering thermogenesis. Primarily found in animals and human infants, the presence of significant brown adipose tissue was identified only recently, and its metabolic role in adults was reconsidered. BAT is believed to have an important role in many metabolic diseases, such as obesity and diabetes, and also to be associated with cancer cachexia. Therefore, it is currently a topic of great interest in the research community, and many groups are investigating the mechanisms underlying BAT metabolism in normal and pathological conditions. However, well established non-invasive methods for assessing BAT distribution and function are still lacking. The purpose of this review is to summarize the current state of the art of these methods, with a particular focus on PET, CT and MRI.
| Type: | Article |
|---|---|
| Title: | Non-invasive Imaging Methods for Brown Adipose Tissue Detection and Function Evaluation |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.4172/2165-8048.1000299 |
| Publisher version: | http://dx.doi.org/10.4172/2165-8048.1000299 |
| Language: | English |
| Additional information: | © 2018 Zhang Y, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Brown adipose tissue, Imaging, Magnetic resonance, Positron emission tomography, Thermogenesis |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10076035 |
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