This work introduces MedForj, a suite of 3D foundational generative priors based on diffusion models. The MedForj models were trained on
72,659 1~mm isotropic 3D
T1-weighted MRI human brain image volumes from
38,174 subjects, drawn from a curated corpus of
80,675 volumes from
42,506 subjects spanning
38 publicly available datasets. These training images were manually inspected to exclude those with poor quality and excessive pathology, and otherwise were minimally processed. The models include six different diffusion training strategies: rectified flow, latent diffusion rectified flow, flow matching, velocity prediction, clean prediction, and noise prediction. Image samples produced by each of these models were compared to each other and against real, ground truth data under downstream segmentation distributions, FID, five inverse problems, and blind human inspection in an observer study. Flow matching was the strongest strategy overall, achieving the best inverse problem solving results at
28.80~dB PSNR and
0.874 SSIM averaged over the five forward problems, the highest rate of reconstructions judged real by blind human raters at
72.6%, and the closest per-structure match to real segmented anatomy in a permutation test. It was not best everywhere: rectified flow produced the most convincing unconditional samples in the observer study and the best FID, and the latent rectified-flow model achieved the smallest joint distributional distance to real anatomy. No other strategy, however, performed consistently well across all four evaluations. We therefore recommend flow matching as the default MedForj prior, while releasing every strategy so that the choice can be revisited per application. All model weights and corresponding code are publicly available at https://github.com/piksl-research/medforj.