单模态化极度异构的多模态医学图像配准
Mono-Modalizing Extremely Heterogeneous Multi-Modal Medical Image Registration
AI总结：
针对极度异构多模态医学图像配准中相似性度量失效的问题，提出M2M-Reg框架，仅用单模态相似性训练并引入GradCyCon正则化器，在ADNI数据集上实现DSC翻倍且支持半监督学习。
摘要：
在临床实践中，具有功能特征的成像模态（如正电子发射断层扫描（PET）和各向异性分数（FA））通常需要与结构参考（如MRI、CT）进行对齐，以便进行准确判读或群组分析，这需要进行多模态可变形图像配准（DIR）。然而，由于这些模态与标准结构扫描相比具有极度异构性，传统的无监督DIR方法难以学习可靠的空间映射，并经常导致图像失真。我们发现，指导这些模型的相似性度量无法捕捉高度不同模态之间的对齐。为了解决这个问题，我们提出了M2M-Reg（Multi-to-Mono Registration），这是一个新颖的框架，仅使用单模态相似性来训练多模态DIR模型，同时保留已建立的架构范式，以便无缝集成到现有模型中。我们还引入了GradCyCon，这是一种利用M2M-Reg循环训练方案来促进微分同胚的正则化器。此外，我们的框架自然地扩展到半监督设置，仅整合预对齐和未对齐的图像对，而不需要真实变换或分割掩码。在阿尔茨海默病神经影像学计划（ADNI）数据集上的实验表明，M2M-Reg在PET-MRI和FA-MRI配准中实现的DSC比先前方法高出2倍，突显了其在处理高度异构多模态DIR方面的有效性。我们的代码可在https://github.com/MICV-yonsei/M2M-Reg获取。
英文摘要：
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 https://github.com/MICV-yonsei/M2M-Reg.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
