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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2509.06592 |
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| _version_ | 1866916939763286016 |
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| author | Scholz, Daniel Erdur, Ayhan Can Holland, Robbie Ehm, Viktoria Peeken, Jan C. Wiestler, Benedikt Rueckert, Daniel |
| author_facet | Scholz, Daniel Erdur, Ayhan Can Holland, Robbie Ehm, Viktoria Peeken, Jan C. Wiestler, Benedikt Rueckert, Daniel |
| contents | Magnetic resonance imaging (MRI) is an invaluable tool for clinical and research applications. Yet, variations in scanners and acquisition parameters cause inconsistencies in image contrast, hindering data comparability and reproducibility across datasets and clinical studies. Existing scanner harmonization methods, designed to address this challenge, face limitations, such as requiring traveling subjects or struggling to generalize to unseen domains. We propose a novel approach using a conditioned diffusion autoencoder with a contrastive loss and domain-agnostic contrast augmentation to harmonize MR images across scanners while preserving subject-specific anatomy. Our method enables brain MRI synthesis from a single reference image. It outperforms baseline techniques, achieving a +7% PSNR improvement on a traveling subjects dataset and +18% improvement on age regression in unseen. Our model provides robust, effective harmonization of brain MRIs to target scanners without requiring fine-tuning. This advancement promises to enhance comparability, reproducibility, and generalizability in multi-site and longitudinal clinical studies, ultimately contributing to improved healthcare outcomes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_06592 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Contrastive Anatomy-Contrast Disentanglement: A Domain-General MRI Harmonization Method Scholz, Daniel Erdur, Ayhan Can Holland, Robbie Ehm, Viktoria Peeken, Jan C. Wiestler, Benedikt Rueckert, Daniel Image and Video Processing Computer Vision and Pattern Recognition Magnetic resonance imaging (MRI) is an invaluable tool for clinical and research applications. Yet, variations in scanners and acquisition parameters cause inconsistencies in image contrast, hindering data comparability and reproducibility across datasets and clinical studies. Existing scanner harmonization methods, designed to address this challenge, face limitations, such as requiring traveling subjects or struggling to generalize to unseen domains. We propose a novel approach using a conditioned diffusion autoencoder with a contrastive loss and domain-agnostic contrast augmentation to harmonize MR images across scanners while preserving subject-specific anatomy. Our method enables brain MRI synthesis from a single reference image. It outperforms baseline techniques, achieving a +7% PSNR improvement on a traveling subjects dataset and +18% improvement on age regression in unseen. Our model provides robust, effective harmonization of brain MRIs to target scanners without requiring fine-tuning. This advancement promises to enhance comparability, reproducibility, and generalizability in multi-site and longitudinal clinical studies, ultimately contributing to improved healthcare outcomes. |
| title | Contrastive Anatomy-Contrast Disentanglement: A Domain-General MRI Harmonization Method |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.06592 |