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°, 45°, 90°, 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 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 mm on our data split. We also introduce FractMorph-Light, a lightweight variant of our model with only 29.6M parameters, which maintains the superior accuracy of the main model while using approximately half the memory. 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 of our implementation is available at this https URL.
Abstract (translated)
可变形图像配准（DIR）是医学影像中对齐解剖结构的一项关键且具有挑战性的技术，广泛应用于多种临床应用。然而，现有的方法往往难以在统一框架内同时捕捉精细的局部变形和大规模的整体变形。我们提出了一种新型的3D双平行变压器架构——FractMorph，该架构通过多域分数傅立叶变换（FrFT）分支增强了跨图像特征匹配。每个分数交叉注意力（FCA）模块以0°、45°、90°等不同角度同时应用并行的分数傅立叶变换，并辅以对数幅度支路，从而能够有效地同时提取局部、半全局和整体特征。这些特征通过固定图像流与移动图像流之间的交叉注意机制进行融合。随后，一个轻量级U-Net风格网络从变压器增强后的特征中预测密集变形场。 在ACDC心脏MRI数据集上，FractMorph实现了最先进的性能，总体Dice相似系数（DSC）为86.45%，平均结构DSC为75.15%，95%的豪斯多夫距离（HD95）为1.54毫米。我们还引入了FractMorph-Light，这是我们的模型的一种轻量级变体，参数仅为29.6百万，在使用大约一半内存的同时保持了主要模型的优异精度。 我们的结果表明，变压器中的多域频谱空间注意能够稳健且高效地建模医学图像中的复杂非刚性变形，并可通过单个端到端网络实现这一目标，无需针对特定场景进行调整或采用分层多尺度网络。我们实施的源代码可在提供的链接中获得。
URL
https://arxiv.org/abs/2508.12445
