DL-based tractography with spatial guidance for public (non-)pathological datasets
Abstract
This record provides pretrained bundle-specific recurrent neural network tractography models and derived streamline tractograms for the arcuate fasciculus (AF), corticospinal tract (CST), and optic radiation (OR). The release includes diffusion-only and spatial-coordinate-augmented gated recurrent unit (GRU) models. Models were trained on the TractoInferno training split and evaluated on the TractoInferno test set, the BTC preoperative brain-tumor dataset, and a subset of the UCSF-PDGM dataset. Resulting tractograms are provided separately as MRtrix .tck files. Input data and preprocessing All diffusion MRI data were harmonized to a common Repulsion100 acquisition scheme with 100 gradient directions using spherical-harmonic resampling. Diffusion-weighted images were normalized by the corresponding b = 0 s/mm² image. At each streamline point, the model receives interpolated local diffusion information: diffusion-only input: (batch_size, 1, 100) diffusion and spatial-coordinate input: (batch_size, 1, 103) For coordinate-augmented models, three normalized spatial coordinates are appended to the diffusion signal. Coordinates were obtained by deformable atlas registration of T1-weighted MRI to the MNI152 atlas. The resulting atlas-space coordinates were normalized to the range [0, 1]. For BTC, only the b = 1200 s/mm² shell was used to better match the TractoInferno acquisitions, which use b-values between 700 and 1000 s/mm². Model architecture and training The models use…
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