MDKFusion: Medical Domain Knowledge-Inspired Area Amplification Network for Multi-Sequence MRI Image Fusion in Ischemic Stroke

Min Li, Pahati Tuxunjiang, Feng Li, Enguang Zuo, Xiaoyi Lv, Yunling Wang, Chen Chen, Cheng Chen

Abstract: Multi-sequence MRI image fusion technology aids radiologists in quickly and accurately assessing ischemic lesions and their surrounding areas by combining DWI and FLAIR images to generate information-rich fusion images. Despite the rapid development of medical image fusion techniques, existing methods are predominantly focused on technical-level model optimization and fail to effectively integrate medical domain knowledge. This limitation reduces their clinical applicability and model interpretability. Inspired by radiologists' diagnostic pattern, which involves focusing on and enlarging lesion areas, we propose a medical domain knowledge-inspired area amplification network for multi-sequence MRI image fusion in ischemic stroke, named MDKFusion. Specifically, we design the Lesion Area Amplification (LAA) module, which uses bicubic interpolation for adaptive amplification and incorporates crosslevel and neighboring-level feature mapping with high-level feature co-guidance. This design emulates radiologists' practice of zooming in to examine lesions, thereby enhancing interpretability. Additionally, we employ the Feature Guidance Module (FGM) to achieve progressive guidance and feature integration. We further introduce the ℒSCD loss function to minimize pixel discrepancies between source and fused images, improving fusion quality. Compared to various mainstream fusion methods, MDKFusion achieves state-of-the-art (SOTA) performance across eight objective evaluation metrics. To confirm its practical value in clinical diagnosis, we invited five radiologists to perform a subjective evaluation of the fused images. Our code will be available at https://github.com/MinLila/MDKFusion.