7 Tesla MRI Liver Fat Quantification in Mice: Data Quality Assessment.

Abstract

The objective of this study was to evaluate the robustness of proton density fat fraction (PDFF) data determined by magnetic resonance imaging (MRI) and spectroscopy (MRS) via spatially resolved error estimation. Using standard T2* relaxation time measurement protocols, in-vivo and ex-vivo MRI data with water and fat nominally in phase or out of phase relative to each other were acquired on a 7 T small animal scanner. Based on a total of 24 different echo times, PDFF maps were calculated in a magnitude-based approach. After identification of the decisive error-prone variables, pixel-wise error estimation was performed by simple propagation of uncertainty. The method was then used to evaluate PDFF data acquired for an explanted mouse liver and an in vivo mouse liver measurement. The determined error maps helped excluding measurement errors as cause of unexpected local PDFF variations in the explanted liver. For in vivo measurements, severe error maps gave rise to doubts in the acquired PDFF maps and triggered an in-depth analysis of possible causes, yielding abdominal movement or bladder filling as in vivo occurring reasons for the increased errors. The combination of pixel-wise acquisition of PDFF data and the corresponding error maps allows for a more specific, spatially resolved evaluation of the PDFF value reliability.