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Spectral Parameter Estimation in the Presence of Macroscopic B0 Variations Using Fast Chemical Shift Imaging

C MacLellan

C MacLellan1,2*, J Hazle1,2, R Stafford1,2, (1) Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas, (2) The University of Texas Graduate School of Biomedical Sciences at Houston, Houston, Texas

WE-C-116-3 Wednesday 10:30AM - 12:30PM Room: 116

Purpose: To assess the ability to correct for macroscopic Bâ‚€ variations to obtain accurate estimates of T2* and amplitude in voxels containing multiple chemical species.

Methods: A solid lipid/water phantom was constructed by adding 1.5% (w/v) agarose and 1.4% (w/v) lecithin to a mixture containing equal parts water and lard by volume. The phantom was imaged using fast multi-echo GRE sequence (TEmin=3.8ms , ΔTE=2 ms, 10 echoes) while a linear gradient was applied in the slice direction at six linearly spaced values ranging from 32 to 200 μT/m. The decay of the complex data was corrected assuming an ideal (boxcar) slice profile and Gaussian slice profile estimated from phantom measurement. At each gradient strength, the T2* and amplitude of both chemical species was estimated using an autoregressive moving average (ARMA) model on the corrected and uncorrected data.

Results: Before correction, the observed decrease in T2* over the range of gradient strengths was greater in water (ΔT2*=-37%) compared to fat (ΔT2*=-23%ms). The Gaussian slice profile assumption led to a better estimation of T2* (ΔT2*=-7% (water), +3%ms (fat) ) compared to the ideal slice profile assumption (ΔT2*=-18% (water); -9%ms (fat) ). Without any correction, the water fraction was overestimated by 2.8% compared to .7% and 1.1% for the Gaussian and boxcar slice profile assumptions, respectively.

Conclusion: The method described in this work allows the T2* and amplitude of multiple chemical species to be corrected for the effects of linear Bâ‚€ variations in the slice direction. In general, Bâ‚€ variations have a greater effect on water T2* than fat T2*. The Gaussian slice profile provides a more robust correction for both T2* and amplitude estimates. Future work will focus on investigating the sensitivity of this model over a range of spectral parameters.

Funding Support, Disclosures, and Conflict of Interest: This research is supported in part by the MD Anderson Cancer Center Support Grant CA016672 and the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number TL1TR000369.

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