Radiology Report Generation (RRG) automates the creation of radiology reports from medical imaging, enhancing the efficiency of the reporting process. Longitudinal Radiology Report Generation (LRRG) extends RRG by incorporating the ability to compare current and prior exams, facilitating the tracking of temporal changes in clinical findings. Existing LRRG approaches only extract features from prior and current images using a visual pre-trained encoder, which are then concatenated to generate the final report. However, these methods struggle to effectively capture both spatial and temporal correlations during the feature extraction process. Consequently, the extracted features inadequately capture the information of difference across exams and thus underrepresent the expected progressions, leading to sub-optimal performance in LRRG. To address this, we develop a novel dynamic difference-aware temporal residual network (DDaTR). In DDaTR, we introduce two modules at each stage of the visual encoder to capture multi-level spatial correlations. The Dynamic Feature Alignment Module (DFAM) is designed to align prior features across modalities for the integrity of prior clinical information. Prompted by the enriched prior features, the dynamic difference-aware module (DDAM) captures favorable difference information by identifying relationships across exams. Furthermore, our DDaTR employs the dynamic residual network to unidirectionally transmit longitudinal information, effectively modelling temporal correlations. Extensive experiments demonstrated superior performance over existing methods on three benchmarks, proving its efficacy in both RRG and LRRG tasks.

翻译：放射学报告生成（RRG）通过医学影像自动生成放射学报告，提升了报告流程的效率。纵向放射学报告生成（LRRG）在RRG基础上扩展了对比当前与既往检查的能力，有助于追踪临床发现的时间变化。现有LRRG方法仅通过视觉预训练编码器从既往和当前影像中提取特征，随后将其拼接以生成最终报告。然而，这些方法在特征提取过程中难以有效捕捉空间与时序相关性。因此，提取的特征未能充分捕获跨检查的差异信息，导致对预期进展的表征不足，从而限制了LRRG的性能。为解决此问题，我们提出了一种新颖的动态差异感知时序残差网络（DDaTR）。在DDaTR中，我们在视觉编码器的每个阶段引入两个模块以捕捉多层次空间相关性。动态特征对齐模块（DFAM）旨在跨模态对齐既往特征，以保持既往临床信息的完整性。在增强的既往特征引导下，动态差异感知模块（DDAM）通过识别跨检查关联来捕捉有效的差异信息。此外，DDaTR采用动态残差网络单向传递纵向信息，有效建模时序相关性。大量实验表明，该方法在三个基准数据集上均优于现有方法，证明了其在RRG和LRRG任务中的有效性。