Abstract #2884
Fully automated processing of multi-echo spectroscopy data for liver fat quantification
Diego Hernando 1 , Nathan S. Artz 1 , Gavin Hamilton 2 , Alejandro Roldan 1 , and Scott B. Reeder 1,3
1
Radiology, University of Wisconsin-Madison,
Madison, WI, United States,
2
Radiology,
University of California, San Diego, San Diego, CA,
United States,
3
Medicine,
University of Wisconsin-Madison, Madison, WI, United
States
Multi-echo liver spectroscopy enables rapid and robust
fat quantification. However, post-processing of
spectroscopy data is often cumbersome, requiring manual
interaction (ie: semi-automatic), which has precluded
its widespread dissemination. In this work, we have
developed a fully automated algorithm for fat
quantification from multi-echo spectroscopy datasets.
This algorithm performs simultaneous fitting of spectra
at multiple echo times. We have validated this automated
algorithm on 425 datasets (1.5T: 152 datasets, 3T: 273
datasets), by comparing its results to the current
semi-automatic technique. Excellent correlation and
agreement was observed at both field strengths,
demonstrating the potential of this approach for
spectroscopy-based fat quantification.
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