Abstract
In clinical practice, imaging modalities with functional characteristics, such as positron emission tomography (PET) and fractional anisotropy (FA), are often aligned with a structural reference (e.g., MRI, CT) for accurate interpretation or group analysis, necessitating multi-modal deformable image registration (DIR). However, due to the extreme heterogeneity of these modalities compared to standard structural scans, conventional unsupervised DIR methods struggle to learn reliable spatial mappings and often distort images. We find that the similarity metrics guiding these models fail to capture alignment between highly disparate modalities. To address this, we propose M2M-Reg (Multi-to-Mono Registration), a novel framework that trains multi-modal DIR models using only mono-modal similarity while preserving the established architectural paradigm for seamless integration into existing models. We also introduce GradCyCon, a regularizer that leverages M2M-Reg's cyclic training scheme to promote diffeomorphism. Furthermore, our framework naturally extends to a semi-supervised setting, integrating pre-aligned and unaligned pairs only, without requiring ground-truth transformations or segmentation masks. Experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrate that M2M-Reg achieves up to 2x higher DSC than prior methods for PET-MRI and FA-MRI registration, highlighting its effectiveness in handling highly heterogeneous multi-modal DIR. Our code is available at this https URL.
Abstract (translated)
在临床实践中，具备功能特性的成像模式（如正电子发射断层扫描(PET)和分数各向异性(FA)）通常需要与结构参考图像（例如MRI或CT）对齐以进行准确的解读或组分析，这需要多模态可变形图像配准(DIR)技术。然而，由于这些功能成像模式相对于标准结构扫描具有极大的异质性，传统的无监督DIR方法难以学习可靠的空域映射，并且经常会扭曲图像。我们发现指导这些模型相似度度量的方法无法捕捉到高度不同模态之间的对齐。 为解决这一问题，我们提出了M2M-Reg（Multi-to-Mono Registration），这是一种新型框架，使用单一模式的相似性来训练多模式DIR模型，同时保持已建立的架构范式以无缝集成现有模型中。此外，我们还引入了GradCyCon，一种正则化器，利用M2M-Reg的循环训练方案促进微分同胚（diffeomorphism）。我们的框架自然地扩展到了半监督设置下，仅整合预先对齐和未对齐的配对数据，并且无需真实转换或分割掩模。 在阿尔茨海默病神经成像倡议(ADNI)数据集上的实验表明，M2M-Reg相较于先前的方法，在PET-MRI及FA-MRI配准中实现了高达两倍的DSC（Dice相似系数），突显了其在处理高度异质性多模式DIR方面的有效性。我们的代码可在提供的链接处获取。
URL
https://arxiv.org/abs/2506.15596
