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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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