Diffusion tensor imaging (DTI) provides crucial insights into the
microstructure of the human brain, but it can be time-consuming to acquire
compared to more readily available T1-weighted (T1w) magnetic resonance imaging
(MRI). To address this challenge, we propose a diffusion bridge model for 3D
brain image translation between T1w MRI and DTI modalities. Our model learns to
generate high-quality DTI fractional anisotropy (FA) images from T1w images and
vice versa, enabling cross-modality data augmentation and reducing the need for
extensive DTI acquisition. We evaluate our approach using perceptual
similarity, pixel-level agreement, and distributional consistency metrics,
demonstrating strong performance in capturing anatomical structures and
preserving information on white matter integrity. The practical utility of the
synthetic data is validated through sex classification and Alzheimer’s disease
classification tasks, where the generated images achieve comparable performance
to real data. Our diffusion bridge model offers a promising solution for
improving neuroimaging datasets and supporting clinical decision-making, with
the potential to significantly impact neuroimaging research and clinical
practice.

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