标题： 用于超声图像解释和扫描引导的语义场景图 显示英文标题

标题： Semantic Scene Graph for Ultrasound Image Explanation and Scanning Guidance

摘要： 由于成像和采集参数的差异导致的显著视觉变化，理解医学超声成像仍然是一个长期存在的挑战。 最近，大型语言模型（LLMs）的进步已被用于自动生成面向具有足够生理知识的临床医生的术语丰富的摘要。 然而，非专业用户（例如，在床旁设置中）对提高超声可解释性和基本扫描指导的日益增长的需求尚未被探索。 在本研究中，我们首先引入了用于超声图像的场景图（SG），以向普通用户解释图像内容并提供超声扫描指导。 超声SG首先使用基于变压器的一阶段方法进行计算，消除了显式目标检测的需要。 为了为普通用户提供可理解的图像解释，然后使用用户查询通过LLMs进一步细化抽象的SG表示。 此外，预测的SG被探索其在引导超声扫描朝向当前成像视图中缺失解剖结构的潜力，帮助普通用户实现更标准化和完整的解剖探索。 该基于SG的图像解释和扫描指导的有效性已在来自左颈和右颈区域（包括颈动脉和甲状腺）的图像上得到验证，涉及五名志愿者。 结果表明，该方法有潜力最大程度地普及超声检查，通过增强其对普通用户的可解释性和可用性。 显示英文摘要

摘要： Understanding medical ultrasound imaging remains a long-standing challenge due to significant visual variability caused by differences in imaging and acquisition parameters. Recent advancements in large language models (LLMs) have been used to automatically generate terminology-rich summaries orientated to clinicians with sufficient physiological knowledge. Nevertheless, the increasing demand for improved ultrasound interpretability and basic scanning guidance among non-expert users, e.g., in point-of-care settings, has not yet been explored. In this study, we first introduce the scene graph (SG) for ultrasound images to explain image content to ordinary and provide guidance for ultrasound scanning. The ultrasound SG is first computed using a transformer-based one-stage method, eliminating the need for explicit object detection. To generate a graspable image explanation for ordinary, the user query is then used to further refine the abstract SG representation through LLMs. Additionally, the predicted SG is explored for its potential in guiding ultrasound scanning toward missing anatomies within the current imaging view, assisting ordinary users in achieving more standardized and complete anatomical exploration. The effectiveness of this SG-based image explanation and scanning guidance has been validated on images from the left and right neck regions, including the carotid and thyroid, across five volunteers. The results demonstrate the potential of the method to maximally democratize ultrasound by enhancing its interpretability and usability for ordinaries.