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.

翻译：医学图像融合通过整合多模态医学图像的互补信息，协助医疗专业人员对患者疾病进行临床诊断，并在术前和术中提供指导。深度学习模型已实现高度鲁棒且精准的端到端图像融合性能。然而，大多数基于深度学习的融合模型对输入图像进行降采样以最小化可学习参数和计算量。在此过程中，源图像的显著特征变得不可恢复，导致关键诊断边缘细节和不同脑组织对比度的损失。本文提出一种基于拉普拉斯-高斯级联与注意力池化集成的新型多模态医学图像融合模型。我们证明该模型能有效保留互补信息和重要组织结构。