Multimodal medical image fusion (MMIF) integrates complementary information from different imaging modalities to enhance image quality and remove redundant data, benefiting a variety of clinical applications such as tumor detection and organ delineation. However, existing MMIF methods often struggle to preserve sharp edges and maintain high contrast, both of which are critical for accurate diagnosis and treatment planning. To address these limitations, this paper proposes ECFusion, a novel MMIF framework that explicitly incorporates edge prior information and leverages a cross-scale transformer. First, an Edge-Augmented Module (EAM) employs the Sobel operator to extract edge features, thereby improving the representation and preservation of edge details. Second, a Cross-Scale Transformer Fusion Module (CSTF) with a Hierarchical Cross-Scale Embedding Layer (HCEL) captures multi-scale contextual information and enhances the global consistency of fused images. Additionally, a multi-path fusion strategy is introduced to disentangle deep and shallow features, mitigating feature loss during fusion. We conduct extensive experiments on the AANLIB dataset, evaluating CT-MRI, PET-MRI, and SPECT-MRI fusion tasks. Compared with state-of-the-art methods (U2Fusion, EMFusion, SwinFusion, and CDDFuse), ECFusion produces fused images with clearer edges and higher contrast. Quantitative results further highlight improvements in mutual information (MI), structural similarity (Qabf, SSIM), and visual perception (VIF, Qcb, Qcv).