Abstract #3273

Analysis of Bias with SNR in Multi-echo Chemical Shift Encoded Fat Quantification

James H Holmes¹, Diego Hernando², Kang Wang¹, Ann Shimakawa³, Nate Roberts², and Scott B Reeder^2,4,5,6

¹MR Applications and Workflow, GE Healthcare, Madison, WI, United States, ²Radiology, University of Wisconsin-Madison, Madison, WI, United States, ³MR Applications and Workflow, GE Healthcare, Menlo Park, CA, United States, ⁴Medical Physics, University of Wisconsin-Madison, Madison, WI, United States, ⁵Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States, ⁶Emergency Medicine, University of Wisconsin-Madison, Madison, WI, United States

Multi-echo chemical shift encoded techniques provide accurate fat quantification over a broad range of fat-fractions and acquisition parameters. In order to be accurate, these techniques must correct for all relevant confounding factors. Emerging applications (eg: fat quantification with high spatial resolution or in iron overloaded tissues) can result in data with significantly lower signal-to-noise ratio (SNR) compared to previously established applications. In this work, we characterize the accuracy of current noise bias correction techniques for fat quantification in the low SNR regime and show simple modifications may enable accurate fat quantification for a wider range of SNR.

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