Characterization of Brown Adipose Tissue using Multi-Varying-Peak MR Spectroscopy (MVP-MRS)
Gregory Simchick1, Jinjian Wu2, Guangming Shi2, Hang Yin3, and Qun Zhao1

1Bioimaging Research Center, University of Georgia, Athens, GA, United States, 2Xidian University, Xian, China, People's Republic of, 3Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA, United States

Synopsis

Differentiation of brown adipose tissue (BAT) from white adipose tissue (WAT) using magnetic resonance imaging (MRI) is clinically important to treat obesity, diabetes and heart disease. In contrast to the traditional Dixon model of single fat- and water-peak or fixed multi-fat-peak models, we propose a Multi-Varying-Peaks MR Spectroscopic (MVP-MRS) model, based on MR imaging data acquired with multiple echoes, to better characterize chemical structures of the fatty acid (saturated vs. unsaturated). Compared with traditional classification of BAT and WAT using fat fraction or proton relaxation time, the proposed MVP-MRS model can achieve a correct classification rate of 95% between BAT and WAT for in vivo mouse data implying great potentials in future longitudinal imaging of BAT and WAT.

Purpose

To better characterize chemical structures of the fatty acid (saturated vs. unsaturated) in brown adipose tissue (BAT), a Multi-Varying-Peaks MR Spectroscopic (MVP-MRS) model is proposed and compared to the traditional Dixon model of single fat- and water- peak or fixed multi-fat-peaks model.

Methods

Identification of the BAT volume and distribution by in vivo imaging is clinically important. Studies were first reported by using PET-CT scans 1. However, the need for using ionizing radiation (18F-FDG) limits applications of PET for longitudinal studies in humans. Previous MRI studies have used chemical shift between muscles and fat to differentiate the two tissues 2-5 or water fat imaging (WFI) using IDEAL algorithm 6. However, these WFI methods assume a fixed multi-peak spectroscopic model for both WAT and BAT and depend on fat fraction ratio and differences of relaxation time to differentiate BAT from WAT7. There have been studies showing that this model is inaccurate when trying to identify BAT using only fat fraction. Representative MR spectra of BAT and WAT showed that, though majority of the spectrum lies in the main peak (-(CH2)n- at 1.3-ppm), there is a substantial amount of spectrum contained with the other fat peaks (e.g. 2.1- and 5.3-ppm, while water peak at 4.7-ppm)7-8. The BAT and WAT spectra show significantly more differences, e.g. relative peak magnitudes and chemical shift (location on the spectrum chart), at a higher magnet field8. Therefore, to differentiate the BAT from WAT, a spectroscopic signal model is needed that approximates the major fat resonances. Here we propose a MVP-MRS model as follows

$$s(t)=(\rho_{w}+\sum_{n=1}^N\beta_{n}e^{j2\pi f_{n}t})e^{j2\pi \phi t} $$

where ρw is the water proton density, fn and βn are the resonant frequency and proton density of the nth fat peak (n=1,…,N) relative to water. Five (N=5) fat peaks, located at 0.9, 1.3, 2.1, 4.3, and 5.3 ppm, were selected, corresponding to different structures of the fatty acid (saturated vs. unsaturated), which are more representative of compositions of the fatty acid8. It is noted that ρw and βn are complex numbers. In order to solve for the (N+1) unknowns (ρw and βn), a total of 2(N+1) measurements of MR signals are needed.

Results

MR experiments were conducted on a 7 T Varian Magnex small animal scanner. MR data was acquired from an oil/butter phantom (a water cylinder embedded with vials of soybean oil, butter, and water) and a 2-month old C57BL6 mouse, approved by IAUCC (University of Georgia), using a 2D gradient-echo sequence with 12 echoes (first TE=5.70 ms, with incremental spacing of 0.525 ms).MR data was analyzed with a modified Matlab water-fat analysis toolbox (http://ismrm.org/workshops/FatWater12/data.htm) using the proposed MVP-MRS model. All components of ρw and βn are normalized to the main/highest fat peak (-(CH2)n- at 1.3 ppm). Figure 1 presents MRI images of the butter/oil and mouse intercapsular water/fat distribution. BAT and WAT areas were identified by their fat fraction (BAT: 50.69% ±6.61%, WAT: 85.10% ±6.84%.)

Figure 2 presents the averages and standard deviations of the 5 fat peaks for the oil/butter data and mouse BAT/WAT data. It was seen that the butter and oil have similar fatty acid structures and homogenous distributions (small standard deviations), while the BAT and WAT have different compositions and inhomogeneous distributions (large standard deviations). To differentiate BAT from WAT, a machine learning algorithm, AdaBoost, is adopted for classification. We randomly selected 80% of the data for classifier training, and the rest (20%) for testing. The train-test procedure was repeated 100 times. Figure 3 presents the classification rates. Since butter and oil have similar fatty acid structures, a classification rate of only 90% was achieved; while the BAT and WAT have different compositions, resulting in a classification rate of 98%.

Discussion and Conclusion

Traditional Dixon or multipeak IDEAL methods requires accurate knowledge of the fat spectrum, including the frequency shifts and the relative amplitudes of the peaks. However, the fat spectrum may likely change from subject to subject and sequence to sequence with different T1 and T2 weightings9-10. Additionally, due to inhomogeneous distribution of the BAT and WAT, and partial volume effect resulting from limited spatial resolution, a fixed-peaks signal model can no longer unbiasedly represent the fat spectrum. Our MR imaging-based MVP spectral model is built upon high spatial resolution MR images, so it is therefore capable of providing high resolution spatial distribution of BAT in the tissues of interest. This will also allow for a longitudinal monitoring of WAT beiging, which is induced by either a pharmaceutical or physical approach, e.g. exposure to cold environment or exercising.

Acknowledgements

The authors wish to thank the grant support of National Institute of Health grant (S10RR023706), Dr. Khan Hekmatyar for assistance in MR data acquisition, and Dr. Houchun Hu, Phoenix Children’s Hospital, AZ, for helpful discussions and assistance in MR data analysis, .

References

1. Cypess, A.M., et al. Identification and importance of brown adipose tissue in adult humans. N Engl J Med 360, 1509-1517 (2009). 2. Dixon, W.T. Simple proton spectroscopic imaging. Radiology 153, 189-194 (1984). 3. Glover, G.H. & Schneider, E. Three-point Dixon technique for true water/fat decomposition with B0 inhomogeneity correction. Magn Reson Med 18, 371-383 (1991). 4. Xiang, Q.S. & An, L. Water-fat imaging with direct phase encoding. J Magn Reson Imaging 7, 1002-1015 (1997). 5. Ma, J. Breath-hold water and fat imaging using a dual-echo two-point Dixon technique with an efficient and robust phase-correction algorithm. Magn Reson Med 52, 415-419 (2004). 6. Yu, H., et al. Multiecho water-fat separation and simultaneous R2* estimation with multifrequency fat spectrum modeling. Magn Reson Med 60, 1122-1134 (2008). 7. Hamilton, G., Smith, D.L., Jr., Bydder, M., Nayak, K.S. & Hu, H.H. MR properties of brown and white adipose tissues. J Magn Reson Imaging 34, 468-473 (2011). 8. Zancanaro, C., et al. Magnetic resonance spectroscopy investigations of brown adipose tissue and isolated brown adipocytes. J Lipid Res 35, 2191-2199 (1994). 9. Bydder, M., et al. Relaxation effects in the quantification of fat using gradient echo imaging. Magn Reson Imaging 26, 347-359 (2008). 10. Wehrli, F.W., Ma, J., Hopkins, J.A. & Song, H.K. Measurement of R'2 in the presence of multiple spectral components using reference spectrum deconvolution. J Magn Reson 131, 61-68 (1998)

Figures

Figure 1: MR images of phantom (left) embedded with 3 vials of oil, butter, and water, and mouse chest (right ) showing highlighted BAT and WAT area

Figure 2: MVP-MRS fat/water peaks illustrating differences of fatty acid compositions for the butter/oil (left) and BAT/WAT (right)

Figure 3: Classification rates of the butter/oil (left) and BAT/WAT (right) data using Adaboost algorithms.



Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
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