Keywords: In Silico, Relaxometry, Low-Field MRI, Hepatic Steatosis and Iron Overload, HIC, Fat Fraction
Motivation: Multi-spectral fat water models fail to produce reliable fat fraction(FF) estimations for severe iron overload conditions at 1.5T and 3T. Low-field MRIs(<1T) may increase the accuracy in HIC and FF estimations at high iron overload by slowing signal decay but might suffer from lower signal-to-noise ratio(SNR).
Goal(s): Assess the accuracy and robustness of quantifying R2* and FF at 0.75T across various SNR conditions.
Approach: Realistic virtual liver models with concomitant presence of iron overload and hepatic steatosis were used to simulate MRI signals at 0.75T and 1.5T using Monte Carlo simulations.
Results: 0.75T showed improved FF and R2* estimation compared to 1.5T.
Impact: Low-field MRI can increase the accuracy and precision in simultaneous quantification of R2* and FF in the presence of mild-to-severe iron overload. With low-field MRI systems being less expensive and potentially increasing MRI accessibility, they can facilitate the reliable diagnosis.
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