基于小波与局部邻域方差的自适应CT与MRI图像融合用于鼻咽癌

文献信息

中文摘要

鼻咽癌（NPC）的多模态成像需要对CT来源的骨性解剖结构与MRI来源的软组织对比度进行互补性呈现。现有融合方法可能无法以透明且可重复的方式同时保留结构细节与软组织信息。我们提出一种基于小波的融合框架，采用局部邻域方差进行自适应加权。这种可解释的方差驱动策略根据区域信息含量选择性地整合CT骨性结构与MRI软组织纹理。邻域配置通过232例临床病例的消融分析进行选择。定量评估显示，在熵、空间频率、平均梯度、标准差和边缘信息保留因子方面，所提方法相较于所选对比方法具有更优的图像质量表现，相对于各指标表现最佳的基线分别提升约5.5%、2.4%、6.7%、5.1%和3.2%。这些发现支持所提方法作为一种保留互补多模态信息的图像融合方法学；其对靶区勾画、工作流程效率或剂量学结局的直接影响尚需未来临床验证。

英文摘要

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.