Table of Contents Author Guidelines Submit a Manuscript
Contrast Media & Molecular Imaging
Volume 2018 (2018), Article ID 6134186, 7 pages
Research Article

Spectral Unmixing Imaging for Differentiating Brown Adipose Tissue Mass and Its Activation

1Molecular Imaging Laboratory, MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Room 2301, Building 149, Charlestown, Boston, MA 02129, USA
2School of Pharmacy, Soochow University, Suzhou 215006, China
3Center for Drug Discovery, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China

Correspondence should be addressed to Chongzhao Ran

Received 30 August 2017; Revised 5 December 2017; Accepted 11 December 2017; Published 4 January 2018

Academic Editor: Giancarlo Pascali

Copyright © 2018 Jing Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Recent large-scale clinical analysis indicates that brown adipose tissue (BAT) mass levels inversely correlate with body-mass index (BMI), suggesting that BAT is associated with metabolic disorders such as obesity and diabetes. PET imaging with 18F-FDG is the most commonly used method for visualizing BAT. However, this method is not able to differentiate between BAT mass and BAT activation. This task, in fact, presents a tremendous challenge with no currently existing methods to separate BAT mass and BAT activation. Our previous results indicated that BAT could be successfully imaged in mice with near infrared fluorescent (NIRF) curcumin analogues. However, the results from conventional NIRF imaging could not reflect what portion of the NIRF signal from BAT activation contributed to the signal observed. To solve this problem, we used spectral unmixing to separate/unmix NIRF signal from oil droplets in BAT, which represents its mass and NIRF signal from blood, which represents BAT activation. In this report, results from our proof-of-concept investigation demonstrated that spectral unmixing could be used to separate NIRF signal from BAT mass and BAT activation.