χ-sepnet: Deep Neural Network for Magnetic Susceptibility Source Separation.

ABSTRACT: Magnetic susceptibility source separation (χ-separation), an advanced quantitative susceptibility mapping (QSM) method, enables the separate estimation of paramagnetic and diamagnetic susceptibility source distributions in the brain. Similar to QSM, it requires solving the ill-conditioned problem of dipole inversion, suffering from so-called streaking artifacts. Additionally, the method utilizes reversible transverse relaxation ( R2'=R2*-R2$$ {R}_2^{\prime }={R}_2^{\ast }-{R}_2 $$ ) to complement frequency shift information for estimating susceptibility source concentrations, requiring time-consuming data acquisition for R2$$ {R}_2 $$ (e.g., multi-echo spin-echo) in addition to multi-echo GRE data for R2*$$ {R}_2^{\ast } $$ . To address these challenges, we develop a new deep learning netw

SUBMITTER: Kim M 

PROVIDER: S-EPMC11748151 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature