FractMorph：基于分数阶傅里叶的多域Transformer用于可变形图像配准
FractMorph: A Fractional Fourier-Based Multi-Domain Transformer for Deformable Image Registration
AI总结：
针对可变形图像配准难以同时捕捉局部与全局变形的问题，提出FractMorph架构，通过多域分数阶傅里叶变换分支增强特征匹配，在医学图像数据集上实现高精度，且轻量级变体保持性能同时降低复杂度。
摘要：
可变形图像配准（DIR）是医学图像中对齐解剖结构的关键且具有挑战性的技术，广泛应用于多种临床场景。然而，现有方法往往难以在统一框架内同时捕捉细粒度局部变形和大规模全局变形。我们提出FractMorph，一种新颖的3D双并行Transformer架构，通过多域分数阶傅里叶变换（FrFT）分支增强跨图像特征匹配。每个分数阶交叉注意力（FCA）块以0°、45°、90°的分数角度应用并行FrFT，并结合对数幅度分支，同时有效提取局部、半全局和全局特征。这些特征通过固定图像流和移动图像流之间的交叉注意力融合。然后，一个轻量级U-Net风格网络从Transformer增强的特征中预测密集变形场。在患者内ACDC心脏MRI数据集上，FractMorph实现了最先进的性能：整体Dice相似系数（DSC）为86.45%，平均每个结构DSC为75.15%，在我们的数据分割中95百分位Hausdorff距离（HD95）为1.54 mm。FractMorph-Light是我们模型的轻量级变体，仅含29.6M参数，在将模型复杂度减半的同时保持了高精度。此外，我们通过在脑图谱到患者数据集上的稳健性能证明了方法的通用性。我们的结果表明，Transformer中的多域谱空间注意力可以通过单一端到端网络稳健高效地建模医学图像中的复杂非刚性变形，无需场景特定调优或分层多尺度网络。源代码可在https://github.com/shayankebriti/FractMorph获取。
英文摘要：
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.
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