Simulation of realistic brain phantoms for susceptibility tensor imaging.

OBJECTIVE: To propose three Susceptibility Tensor Imaging (STI) brain phantoms as ground truth for evaluating STI reconstruction algorithms: two derived from STI data and the one from Diffusion Tensor Imaging (DTI). METHODS: The eigenvalues were generated through a pipeline inspired by the Quantitative Susceptibility Mapping (QSM) Reconstruction Challenge 2.0. An eigendecomposition was applied to an acquired susceptibility tensor, and literature-reported mean eigenvalues were assigned to 13 distinct brain regions. Fractional Anisotropy (FA) maps provided realistic spatial texture in the eigenvalues. Eigenvectors were obtained from DTI and two STI reconstructions: a Least-Squares algorithm and Diffusion Regularized STI (DRSTI). Microstructural simulations were incorporated into white matter regions. Phantom behavior was tested in three experiments: (i) anisotropic susceptibility assessment using QSM at different orientations; (ii) STI reconstructions across five algorithms with varying orientation numbers; and (iii) reconstructions under different angular rotation ranges. RESULTS: The phantoms showed strong contrast in subcortical regions and realistic microstructural patterns. Algorithmic performance trends matched prior reports, with reduced errors as orientation count and rotation range increased. DISCUSSION: Three open-source, in-silico STI brain phantoms were developed and validated as ground truth references for the evaluation of current and future STI reconstruction algorithms.