Multimodal medical image fusion plays an instrumental role in several areas of medical image processing, particularly in disease recognition and tumor detection. Traditional fusion methods tend to process each modality independently before combining the features and reconstructing the fusion image. However, this approach often neglects the fundamental commonalities and disparities between multimodal information. Furthermore, the prevailing methodologies are largely confined to fusing two-dimensional (2D) medical image slices, leading to a lack of contextual supervision in the fusion images and subsequently, a decreased information yield for physicians relative to three-dimensional (3D) images. In this study, we introduce an innovative unsupervised feature mutual learning fusion network designed to rectify these limitations. Our approach incorporates a Deformable Cross Feature Blend (DCFB) module that facilitates the dual modalities in discerning their respective similarities and differences. We have applied our model to the fusion of 3D MRI and PET images obtained from 660 patients in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Through the application of the DCFB module, our network generates high-quality MRI-PET fusion images. Experimental results demonstrate that our method surpasses traditional 2D image fusion methods in performance metrics such as Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). Importantly, the capacity of our method to fuse 3D images enhances the information available to physicians and researchers, thus marking a significant step forward in the field. The code will soon be available online.

翻译：多模态医学图像融合在医学图像处理的多个领域（尤其是疾病识别与肿瘤检测）中发挥着关键作用。传统融合方法往往先独立处理每种模态，再合并特征并重构融合图像，然而这种方式常常忽略了多模态信息之间的本质共性与差异。此外，现有方法主要局限于融合二维（2D）医学图像切片，导致融合图像缺乏上下文监督信息，进而使医生获取的信息量低于三维（3D）图像。本研究提出了一种创新的无监督特征互学习融合网络，旨在解决上述局限。我们的方法引入了可变形交叉特征融合（DCFB）模块，使双模态能够识别各自的相似性与差异性。我们将该模型应用于阿尔茨海默病神经影像学倡议（ADNI）数据集中660名患者的三维MRI与PET图像融合。通过应用DCFB模块，我们的网络生成了高质量的MRI-PET融合图像。实验结果表明，本方法在峰值信噪比（PSNR）和结构相似性指数（SSIM）等性能指标上均优于传统二维图像融合方法。更重要的是，本方法融合三维图像的能力增强了医生与研究人员可获取的信息，标志着该领域取得了重要进展。代码将很快公开上线。