Synthetic fem pretraining dataset
Abstract
Synthetic pretraining dataset for a physics-informed graph neural network (PI-GNN) model of glioma (brain tumor) growth. Each of the 750 files represents one simulated patient: a Fisher-KPP reaction-diffusion tumor growth trajectory computed with an explicit finite-volume solver over a placeholder synthetic anatomical substrate (not real patient anatomy), with randomized ground-truth diffusion scale and proliferation rate per patient. This dataset was generated by varying the diffusion coefficient (D) and proliferation rate (rho) over a synthetic anatomical substrate, as described in the Methods section of the accompanying paper. File format: one .npz (compressed NumPy archive) per synthetic patient (synthetic_patient_0000.npz ... synthetic_patient_0749.npz). Each file contains:- trajectory: (n_steps+1, 64, 64, 64) float — tumor cell density c(x,t) in [0,1] over the simulated growth window- tissue_mask: (64, 64, 64) int — 0=background, 1=gray matter, 2=white matter- D_tensor: (64, 64, 64, 3, 3) float — per-voxel diffusion tensor used by the solver- D_scale: scalar float — ground-truth global diffusion multiplier for this patient- rho: scalar float — ground-truth proliferation rate for this patient- seed_location: (3,) int — voxel index where tumor growth was seeded- affine: (4, 4) float — identity matrix (no real physical voxel spacing exists for this synthetic substrate; kept for schema consistency with real-anatomy loaders used elsewhere in the pipeline) This dataset…
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