CT report generation (CTRG) aims to automatically generate diagnostic reports for 3D volumes, relieving clinicians' workload and improving patient care. Despite clinical value, existing works fail to effectively incorporate diagnostic information from multiple anatomical views and lack related clinical expertise essential for accurate and reliable diagnosis. To resolve these limitations, we propose a novel Multi-view perception Knowledge-enhanced Tansformer (MvKeTR) to mimic the diagnostic workflow of clinicians. Just as radiologists first examine CT scans from multiple planes, a Multi-View Perception Aggregator (MVPA) with view-aware attention effectively synthesizes diagnostic information from multiple anatomical views. Then, inspired by how radiologists further refer to relevant clinical records to guide diagnostic decision-making, a Cross-Modal Knowledge Enhancer (CMKE) retrieves the most similar reports based on the query volume to incorporate domain knowledge into the diagnosis procedure. Furthermore, instead of traditional MLPs, we employ Kolmogorov-Arnold Networks (KANs) with learnable nonlinear activation functions as the fundamental building blocks of both modules to better capture intricate diagnostic patterns in CT interpretation. Extensive experiments on the public CTRG-Chest-548K dataset demonstrate that our method outpaces prior state-of-the-art models across all metrics.

本研究解决了现有CT报告生成在多解剖视角整合诊断信息和缺乏临床专业知识方面的不足。我们提出了一种新颖的多视角感知知识增强变换器（MvKeTR），模拟临床医生的诊断工作流程，通过多视角感知聚合器有效合成信息，并通过跨模态知识增强器引入相关的临床记录以指导诊断决策。实验表明，该方法在CTRG-Chest-548K数据集上超越了所有现有最先进模型。

MvKeTR：基于多视角感知与知识增强的胸部CT报告生成