Intelligent Ultrasound Imaging

超声（美国）是医学成像行业中发展最快的部门。最近开发的价格实惠的便携式美国扫描仪催生了大量的医疗点美国（POCUS）应用程序，可以改变患者在放射科以外的生活。美国也在寻找新的应用，例如在肌肉骨骼环境中，当涉及到扫描协议时，很少有智慧可用。其中一些涉及徒手3D US，这是一种手动扫过感兴趣区域以获取3D图像的方法。虽然美国技术本身已经成熟，可以解决这些新应用，但由于缺乏熟练的美国成像操作员，进展受到限制。由于超声检查质量高度依赖于操作人员的技能，因此提高可用性是向新用户和新应用部署超声成像的关键，也是本研究计划的目标。为此，我们将开发一种新的测量系统来研究探针运动和凝视模式，这些模式表征了经验丰富的超声医师在他们熟悉或不熟悉的任务中使用的视觉运动策略。我们将使用测量和机器学习方法来研究超声仪使用的与美国视图质量相关的图像特征和视觉线索，以及如何使用这些视觉线索（在超声仪通常看到但未记录的中间美国视图序列中看到）来指导电机决策并将探头导航到给定目标。这些新获得的知识可以为智能美联成像（i-US）带来新的交互式导航和制导系统，将指导经验不足的用户获得高质量的美联图像。使用受移动机器人文献启发的新型同步定位和映射算法，将对目标2D美国视图进行引导导航研究，而我们的徒手3D美国成像自适应引导策略将建立在强化学习技术的最新发展之上，强化学习技术是机器学习方法的一个强大子集，旨在优化顺序决策(在这种情况下，如何在检查过程中移动超声探头以获得所需的高质量体积数据)。我们的研究结果将在青少年特发性脊柱侧凸（AIS）的背景下得到验证，这是一种发生在青少年快速生长期间的脊柱变形，从而在AIS的无辐射评估和随访方面取得进展。本提案中设想的i-US既新颖又非常及时，因为它可以加速新用户和新应用程序对US的采用。事实上，i-US有潜力克服成像技术的缺乏，这是广泛接受极具前景的POCUS技术和徒手3D成像协议的主要障碍，具有提高医疗保健标准的潜力。

Ultrasound (US) is the fastest developing sector in the medical imaging industry. The recent development of affordable and portable US scanners spawned numerous point-of-care US (POCUS) applications that can change patients' lives outside radiology departments. US is also finding new applications, e.g. in the musculoskeletal context, for which there is little wisdom available when it comes to scanning protocols. Some of these involve freehand 3D US, a method by which the US probe is manually swept over an area of interest to acquire 3D images. While US technology itself has reached the maturity to address these new applications, progress is limited by a shortage of skilled US imaging operators. Since US examination quality is highly dependent on the skill of the operator, improving usability is key to the deployment of US imaging towards new users and new applications and constitutes the objective of this research program. To this end, we will develop a novel measurement system to study the probe motion and gaze patterns that characterize the visual-motor strategies used by experienced sonographers in tasks that are well-known or poorly-known to them. We will use the measurements and machine learning methods to investigate the image features and visual cues used by sonographers that are related to US view quality, as well as how these visual cues, as seen in sequences of intermediate US views typically seen but not recorded by sonographers, are used to guide motor decisions and navigate the probe towards a given target.  This newly acquired knowledge could lead to new interactive navigation and guidance systems for intelligent US imaging (i-US) that will guide less experienced users towards high quality US image acquisitions. Guided navigation towards a target 2D US view will be investigated using novel simultaneous localization and mapping algorithms inspired from the mobile robotics literature, while our adaptive guidance strategies for freehand 3D US imaging will build on the most recent developments in reinforcement learning techniques, a powerful subset of machine learning methods that aims at optimizing sequential decision making (in this case, how to move the US probe during an examination to acquire the desired high quality volumetric data). Our findings will be validated in the context of adolescent idiopathic scoliosis (AIS), a deformation of the spine occurring during rapid adolescent growth, thereby making progress towards radiation-free assessment and follow-up of AIS. i-US as envisioned in this proposal is both entirely novel and extremely timely as it could speed up the adoption of US by new users and in new applications.  Indeed, i-US has the potential to overcome the lack of imaging skills that is the primary barrier to broad acceptance of the highly promising POCUS technology and the freehand 3D imaging protocol, with the potential to improve the standard of medical care.