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