{"ID":2881498,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.12445","arxiv_id":"2508.12445","title":"FractMorph: A Fractional Fourier-Based Multi-Domain Transformer for Deformable Image Registration","abstract":"Deformable image registration (DIR) is a crucial and challenging technique for aligning anatomical structures in medical images and is widely applied in diverse clinical applications. However, existing approaches often struggle to capture fine-grained local deformations and large-scale global deformations simultaneously within a unified framework. We present FractMorph, a novel 3D dual-parallel transformer-based architecture that enhances cross-image feature matching through multi-domain fractional Fourier transform (FrFT) branches. Each Fractional Cross-Attention (FCA) block applies parallel FrFTs at fractional angles of $0^\\circ$, $45^\\circ$, $90^\\circ$, along with a log-magnitude branch, to effectively extract local, semi-global, and global features at the same time. These features are fused via cross-attention between the fixed and moving image streams. A lightweight U-Net style network then predicts a dense deformation field from the transformer-enriched features. On the intra-patient ACDC cardiac MRI dataset, FractMorph achieves state-of-the-art performance with an overall Dice Similarity Coefficient (DSC) of $86.45\\%$, an average per-structure DSC of $75.15\\%$, and a 95th-percentile Hausdorff distance (HD95) of $1.54~\\mathrm{mm}$ on our data split. FractMorph-Light, a lightweight variant of our model with only 29.6M parameters, preserves high accuracy while halving model complexity. Furthermore, we demonstrate the generality of our approach with solid performance on a cerebral atlas-to-patient dataset. Our results demonstrate that multi-domain spectral-spatial attention in transformers can robustly and efficiently model complex non-rigid deformations in medical images using a single end-to-end network, without the need for scenario-specific tuning or hierarchical multi-scale networks. The source code is available at https://github.com/shayankebriti/FractMorph.","short_abstract":"Deformable image registration (DIR) is a crucial and challenging technique for aligning anatomical structures in medical images and is widely applied in diverse clinical applications. However, existing approaches often struggle to capture fine-grained local deformations and large-scale global deformations simultaneousl...","url_abs":"https://arxiv.org/abs/2508.12445","url_pdf":"https://arxiv.org/pdf/2508.12445v2","authors":"[\"Shayan Kebriti\",\"Shahabedin Nabavi\",\"Ali Gooya\"]","published":"2025-08-17T17:42:10Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.CV\"]","methods":"[\"Transformer\"]","has_code":false,"code_links":[{"ID":610816,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2881498,"paper_url":"https://arxiv.org/abs/2508.12445","paper_title":"FractMorph: A Fractional Fourier-Based Multi-Domain Transformer for Deformable Image Registration","repo_url":"https://github.com/shayankebriti/FractMorph","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}