MAGORINO: Magnitude-only fat fraction and R^* ₂ estimation with Rician noise modeling.

ABSTRACT:

Purpose

Magnitude-based fitting of chemical shift-encoded data enables proton density fat fraction (PDFF) and R2*$$ {R}_2^{\ast } $$ estimation where complex-based methods fail or when phase data are inaccessible or unreliable. However, traditional magnitude-based fitting algorithms do not account for Rician noise, creating a source of bias. To address these issues, we propose an algorithm for magnitude-only PDFF and R2*$$ {R}_2^{\ast } $$ estimation with Rician noise modeling (MAGORINO).

Methods

Simulations of multi-echo gradient-echo signal intensities are used to investigate the performance and behavior of MAGORINO over the space of clinically plausible PDFF, R2*$$ {R}_2^{\ast } $$ , and SNR values. Fitting performance is assessed through detailed simulation, including li

SUBMITTER: Bray TJP 

PROVIDER: S-EPMC10092287 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature