An FA-based hybrid DTI-mixture model for multi-fiber reconstruction in diffusion MRI.

OBJECTIVE: This study proposes a hybrid diffusion MRI reconstruction framework combining Diffusion Tensor Imaging (DTI) and the multi-compartment non-central Wishart (MNCW) mixture model. The proposed framework uses an empirically selected fractional anisotropy (FA) threshold of 0.75 as a heuristic to distinguish between single- and multi-fiber voxels, enabling adaptive selection of the most appropriate reconstruction model for each voxel. MATERIALS AND METHODS: Synthetic diffusion MRI datasets containing single-, two-, and three-fiber configurations were generated using 82 diffusion-sensitizing gradient directions with Rician noise. Voxels with FA ≥ 0.75 were reconstructed using DTI, while voxels with FA < 0.75 were analyzed using the MNCW model. Angular error and clustering behavior were evaluated under different thresholds. The method was further validated using rat optic chiasm and human brain datasets. RESULTS: The proposed framework reduced incorrect single-fiber detections and produced fewer disoriented voxels in complex fiber configurations compared with the conventional mixture model. A clustering threshold of 17 ∘ provided stable reconstruction without merging distinct fibers. Real data experiments demonstrated anatomically consistent reconstruction of white matter structures. DISCUSSION: The hybrid framework improves orientation estimation by selectively applying mixture modeling only in voxels likely to contain multiple fibers, reducing over-segmentation, improving stability, and lowering computational complexity.