动态超声造影（CEUS）成像可以反映微血管分布和血流灌注情况，对鉴别甲状腺结节的良恶性具有临床意义。值得注意的是，CEUS 可对结节周围的微血管分布进行细致的可视化，与灰度超声 (US) 相比，导致肿瘤大小明显增加。在获得的双图像中，病变尺寸从灰度US放大到CEUS，因为微血管似乎不断浸润周围组织。尽管微脉管系统的浸润性扩张仍然不明确，但超声检查医师认为它可能促进甲状腺结节的诊断。我们提出了一种深度学习模型，旨在模拟超声医师所采用的诊断推理过程。该模型整合了动态 CEUS 上微血管浸润的观察，利用灰度超声提供的额外见解来增强诊断支持。具体来说，在动态CEUS的时间维度上实施时间投影注意力来表示微血管灌注。此外，我们采用一组具有灵活的 Sigmoid Alpha 函数的置信图来感知和描述渗透扩张过程。此外，引入自适应整合机制，动态整合个体患者的辅助灰度超声和CEUS置信图，确保甲状腺结节的诊断可信。在这项回顾性研究中，我们收集了 282 个 CEUS 视频的甲状腺结节数据集。该方法的诊断准确率和灵敏度分别为 89.52% 和 93.75%。这些结果表明，模仿超声医师的诊断思维，包括动态微血管灌注和浸润扩张，被证明有利于基于 CEUS 的甲状腺结节诊断。

Dynamic contrast-enhanced ultrasound (CEUS) imaging can reflect the microvascular distribution and blood flow perfusion, thereby holding clinical significance in distinguishing between malignant and benign thyroid nodules. Notably, CEUS offers a meticulous visualization of the microvascular distribution surrounding the nodule, leading to an apparent increase in tumor size compared to gray-scale ultrasound (US). In the dual-image obtained, the lesion size enlarged from gray-scale US to CEUS, as the microvascular appeared to be continuously infiltrating the surrounding tissue. Although the infiltrative dilatation of microvasculature remains ambiguous, sonographers believe it may promote the diagnosis of thyroid nodules. We propose a deep learning model designed to emulate the diagnostic reasoning process employed by sonographers. This model integrates the observation of microvascular infiltration on dynamic CEUS, leveraging the additional insights provided by gray-scale US for enhanced diagnostic support. Specifically, temporal projection attention is implemented on time dimension of dynamic CEUS to represent the microvascular perfusion. Additionally, we employ a group of confidence maps with flexible Sigmoid Alpha Functions to aware and describe the infiltrative dilatation process. Moreover, a self-adaptive integration mechanism is introduced to dynamically integrate the assisted gray-scale US and the confidence maps of CEUS for individual patients, ensuring a trustworthy diagnosis of thyroid nodules. In this retrospective study, we collected a thyroid nodule dataset of 282 CEUS videos. The method achieves a superior diagnostic accuracy and sensitivity of 89.52% and 93.75%, respectively. These results suggest that imitating the diagnostic thinking of sonographers, encompassing dynamic microvascular perfusion and infiltrative expansion, proves beneficial for CEUS-based thyroid nodule diagnosis.