Intelligent Ultrasound Imaging

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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.