Medical imaging maps tissue characteristics into image intensity values, enhancing human perception. However, comprehending this data, especially in high-stakes scenarios such as surgery, is prone to errors. Additionally, current multimodal methods do not fully leverage this valuable data in their design. We introduce "From Tissue to Sound," a new paradigm for medical sonic interaction design. This paradigm establishes a comprehensive framework for mapping tissue characteristics to auditory displays, providing dynamic and intuitive access to medical images that complement visual data, thereby enhancing multimodal perception. "From Tissue to Sound" provides an advanced and adaptable framework for the interactive sonification of multimodal medical imaging data. This framework employs a physics-based sound model composed of a network of multiple oscillators, whose mechanical properties-such as friction and stiffness-are defined by tissue characteristics extracted from imaging data. This approach enables the representation of anatomical structures and the creation of unique acoustic profiles in response to excitations of the sound model. This method allows users to explore data at a fundamental level, identifying tissue characteristics ranging from rigid to soft, dense to sparse, and structured to scattered. It facilitates intuitive discovery of both general and detailed patterns with minimal preprocessing. Unlike conventional methods that transform low-dimensional data into global sound features through a parametric approach, this method utilizes model-based unsupervised mapping between data and an anatomical sound model, enabling high-dimensional data processing. The versatility of this method is demonstrated through feasibility experiments confirming the generation of perceptually discernible acoustic signals. Furthermore, we present a novel application developed based on this framework for retinal surgery. This new paradigm opens up possibilities for designing multisensory applications for multimodal imaging data. It also facilitates the creation of interactive sonification models with various auditory causality approaches, enhancing both directness and richness.

Retinal surgery is a complex medical procedure that requires exceptional expertise and dexterity. For this purpose, several robotic platforms are currently under development to enable or improve the outcome of microsurgical tasks. Since the control of such robots is often designed for navigation inside the eye in proximity to the retina, successful trocar docking and insertion of the instrument into the eye represents an additional cognitive effort, and is therefore one of the open challenges in robotic retinal surgery. For this purpose, we present a platform for autonomous trocar docking that combines computer vision and a robotic setup. Inspired by the Cuban Colibri (hummingbird) aligning its beak to a flower using only vision, we mount a camera onto the endeffector of a robotic system. By estimating the position and pose of the trocar, the robot is able to autonomously align and navigate the instrument towards the Trocar Entry Point (TEP) and finally perform the insertion. Our experiments show that the proposed method is able to accurately estimate the position and pose of the trocar and achieve repeatable autonomous docking. The aim of this work is to reduce the complexity of the robotic setup prior to the surgical task and therefore, increase the intuitiveness of the system integration into clinical workflow.

While intraoperative imaging is commonly used to guide surgical interventions, automatic robotic support for image-guided navigation has not yet been established in clinical routine. In this paper, we propose a novel visual servoing framework that combines, for the first time, full image-based 3D ultrasound registration with a real-time servo-control scheme. Paired with multi-modal fusion to a pre-interventional plan such as an annotated needle insertion path, it thus allows tracking a target anatomy, continuously updating the plan as the target moves, and keeping a needle guide aligned for accurate manual insertion. The presented system includes a motorized 3D ultrasound transducer mounted on a force-controlled robot and a GPU-based image processing toolkit. The tracking accuracy of our framework is validated on a geometric agar/gelatin phantom using a second robot, achieving positioning errors of on average 0.42-0.44 mm. With compounding and registration runtimes of up to total around 550 ms, real-time performance comes into reach. We also present initial results on a spine phantom, demonstrating the feasibility of our system for lumbar spine injections.