Wavelet-based adaptive image fusion of CT and MRI with local neighborhood variance for nasopharyngeal carcinoma

Multimodal imaging of nasopharyngeal carcinoma (NPC) requires complementary representation of CT-derived bony anatomy and MRI-derived soft-tissue contrast. Existing fusion methods may fail to preserve both structural detail and soft-tissue information in a transparent and reproducible manner. We present a wavelet-based fusion framework that employs local neighborhood variance for adaptive weighting. This interpretable variance-driven strategy selectively integrates CT bony structures and MRI soft-tissue textures according to regional information content. The neighborhood configuration was selected through an ablation analysis of 232 clinical cases. Quantitative evaluation showed improved image-quality performance over the selected comparison methods across entropy, spatial frequency, average gradient, standard deviation, and edge information preservation factor, with respective gains of approximately 5.5%, 2.4%, 6.7%, 5.1%, and 3.2% relative to the best-performing baseline for each metric. These findings support the proposed method as an image fusion methodology for preserving complementary multimodal information; direct effects on target delineation, workflow efficiency, or dosimetric outcomes require future clinical validation.