Sasan Matinfar

Veronica Ruozzi, Sasan Matinfar, Pasquale Vergara et al.

Percutaneous epicardial access (PEA), performed on a beating heart under fluoroscopy, enables arrhythmia treatment. However, advancing a needle toward the thin and moving pericardium remains highly challenging and risky. To address this problem, we present a physics-driven sonification method for Extended Reality (XR)-based multisensory navigation to enhance user perception during the critical needle landing phase in PEA. Dynamic cardiac anatomy from 4D CTA was reconstructed and registered to a real-world coordinate system. Real-time needle tracking provided the position of the needle tip relative to moving cardiac structures and drove an audio-visual feedback module. The visual display presented navigational cues and dynamic anatomy, while the auditory display encoded physiological cardiac states using a multilayer physical membrane model. A phantom study was conducted with twelve cardiologists performing needle insertions under visual-only and multisensory feedback. The multisensory method significantly improved navigation safety ($χ^2 = 11.30$, $p < 0.01$), reducing myocardial contact (3.64% vs. 7.27%) and increasing correct access (90.91% vs. 52.73%). Needle placement accuracy improved, with closer membrane proximity (Cliff delta = 0.19) and reduced variability ($p < 0.05$). Execution time was comparable, while time-accuracy correlations differed significantly between modalities ($p < 0.01$). NASA-TLX indicated lower cognitive load with multisensory guidance ($p < 0.01$). These results demonstrate the feasibility of physics-driven sonification for improving spatiotemporal awareness and supporting user-centered surgical navigation.

Luis D. Reyes Vargas, Veronica Ruozzi, Andrea K. M. Ross et al.

Subretinal injection is a delicate vitreoretinal procedure requiring precise needle placement within the subretinal space while avoiding perforation of the retinal pigment epithelium (RPE), a layer directly beneath the target with extremely limited regenerative capacity. To enhance depth perception during cannula advancement, intraoperative optical coherence tomography (iOCT) offers high-resolution cross-sectional visualization of needle-tissue interaction; however, interpreting these images requires sustained visual attention alongside the en face microscope view, thereby increasing cognitive load during critical phases and placing additional demands on the surgeon's proprioceptive control. In this paper, we propose a structured, real-time sonification framework designed for extensible mapping of iOCT-derived anatomical features into perceptual auditory feedback. The method employs a physics-inspired acoustic model driven by segmented retinal layers from a stream of iOCT B-scans, with needle motion and injection-induced retinal layer displacements serving as excitation inputs to the sound model, enabling perception of tool position and retinal deformation. In a controlled user study (n=34), the proposed sonification achieved high retinal layer identification accuracy and robust detection of retinal deformation-related events, significantly outperforming a state-of-the-art baseline in overall event identification (83.4% vs. 60.6%, p < 0.001), with gains driven primarily by enhanced detection of injection-induced retinal deformation. Evaluation by experts (n=4) confirmed the clinical relevance and potential intraoperative applicability of the method. These results establish structured iOCT sonification as a viable complementary modality for real-time surgical guidance in subretinal injection.

Laura Schütz, Sasan Matinfar, Ulrich Eck et al.

In Augmented Reality (AR), virtual objects interact with real objects. However, the lack of physicality of virtual objects leads to the absence of natural sonic interactions. When virtual and real objects collide, either no sound or a generic sound is played. Both lead to an incongruent multisensory experience, reducing interaction and object realism. Unlike in Virtual Reality (VR) and games, where predefined scenes and interactions allow for the playback of pre-recorded sound samples, AR requires real-time sound synthesis that dynamically adapts to novel contexts and objects to provide audiovisual congruence during interaction. To enhance real-virtual object interactions in AR, we propose a framework for context-aware sounds using methods from computer vision to recognize and segment the materials of real objects. The material's physical properties and the impact dynamics of the interaction are used to generate material-based sounds in real-time using physical modelling synthesis. In a user study with 24 participants, we compared our congruent material-based sounds to a generic sound effect, mirroring the current standard of non-context-aware sounds in AR applications. The results showed that material-based sounds led to significantly more realistic sonic interactions. Material-based sounds also enabled participants to distinguish visually similar materials with significantly greater accuracy and confidence. These findings show that context-aware, material-based sonic interactions in AR foster a stronger sense of realism and enhance our perception of real-world surroundings.