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Abstract #0326

Simultaneous Iron and Fat Quantification Using an Auto Regressive Moving Average Model at 1.5T and 3T

Aaryani Tipirneni-Sajja1, Axel J. Krafft2, Brian Taylor3, Ralf B. Loeffler1, Ruitian Song1, Nathan Artz1, Jane S. Hankins4, and Claudia M. Hillenbrand1

1Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, TN, United States, 2Radiology – Medical Physics, Medical Center – University of Freiburg, Freiburg, Germany, 3Imaging Physics, The University of Texas MD Anderson Cancer Center, TX, United States, 4Hematology, St. Jude Children's Research Hospital, Memphis, TN, United States

A major confounder of hepatic iron assessment by R2*-MRI is fat (e.g. steatosis) which introduces signal modulations. In this study, we systematically evaluate two signal modeling techniques, an autoregressive moving average (ARMA) model and the method provided by the ISMRM Fat-Water Toolbox for simultaneous iron and fat quantification in phantoms and in vivo. Preliminary data suggest that ARMA and Toolbox can be used for iron and fat quantification at 1.5T and 3T. In severe iron-overload cases, both, ARMA and the Toolbox might produce inaccurate FF results, however in vivo ARMA seemed to provide a more robust liver R2* quantification.

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