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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.15267 |
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| _version_ | 1866909586647154688 |
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| author | Zhang, Shaorong Chattopadhyay, Tamoghna Thomopoulos, Sophia I. Ambite, Jose-Luis Thompson, Paul M. Steeg, Greg Ver |
| author_facet | Zhang, Shaorong Chattopadhyay, Tamoghna Thomopoulos, Sophia I. Ambite, Jose-Luis Thompson, Paul M. Steeg, Greg Ver |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_15267 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Diffusion Bridge Models for 3D Medical Image Translation Zhang, Shaorong Chattopadhyay, Tamoghna Thomopoulos, Sophia I. Ambite, Jose-Luis Thompson, Paul M. Steeg, Greg Ver Computer Vision and Pattern Recognition 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. |
| title | Diffusion Bridge Models for 3D Medical Image Translation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.15267 |