FORCE: FORward modeling for Complex microstructure Estimation

Diffusion Magnetic Resonance Imaging (dMRI) is a noninvasive modality that enables the study of brain tissue microstructure and the reconstruction of neural pathways. To achieve this, most reconstruction methods rely on inverse modeling techniques, which are often ill-posed and struggle to resolve shallow fiber crossings. Moreover, existing methods typically focus either on estimating fiber orientations or on deriving microstructural maps. As a result, obtaining a comprehensive characterization of tissue microstructure and architecture often requires combining multiple models, which is computationally demanding, potentially inconsistent due to model-specific assumptions and acquisition settings. This work introduces FORCE, a forward modeling paradigm that reframes how diffusion data is analyzed. Instead of inverting the measured signal, FORCE simulates a large set of biologically plausible intra-voxel fiber configurations and tissue compositions. It then identifies the best-matching simulation for each voxel by operating directly in the signal space. This unified framework simultaneously resolves low-angle fiber crossings, producing a large suite of microstructural maps and complete tissue segmentation in a single process. The proposed approach demonstrates robust performance across synthetic and real datasets from both human and mouse brains, encompassing multiple resolutions and acquisition types.