  Medical image fusion combines the complementary information of multimodal
medical images to assist medical professionals in the clinical diagnosis of
patients' disorders and provide guidance during preoperative and
intra-operative procedures. Deep learning (DL) models have achieved end-to-end
image fusion with highly robust and accurate fusion performance. However, most
DL-based fusion models perform down-sampling on the input images to minimize
the number of learnable parameters and computations. During this process,
salient features of the source images become irretrievable leading to the loss
of crucial diagnostic edge details and contrast of various brain tissues. In
this paper, we propose a new multimodal medical image fusion model is proposed
that is based on integrated Laplacian-Gaussian concatenation with attention
pooling (LGCA). We prove that our model preserves effectively complementary
information and important tissue structures.
