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.

翻译：视网膜下注射是一种精细的玻璃体视网膜手术，需要将针头精确放置于视网膜下腔，同时避免穿透视网膜色素上皮层（RPE），该层位于靶组织正下方，再生能力极为有限。为增强套管推进过程中的深度感知，术中光学相干断层扫描（iOCT）可提供针尖与组织相互作用的高分辨率横截面可视化图像；然而，解读这些图像需在观察平面显微镜视图的同时持续投入视觉注意力，从而在关键手术阶段增加认知负荷，并对外科医生的本体感觉控制提出额外要求。本文提出一种结构化的实时声化框架，旨在将iOCT提取的解剖特征可扩展地映射为感知性听觉反馈。该方法采用受物理启发的声学模型，由iOCT B扫描流中分割的视网膜层驱动，以针头运动及注射引发的视网膜层位移作为声学模型的激励输入，从而实现对器械位置和视网膜变形的感知。在受控用户研究（n=34）中，所提出的声化方法实现了高精度的视网膜层识别及稳健的视网膜形变事件检测，在总体事件识别准确率上显著优于现有最优基线（83.4% vs. 60.6%，p < 0.001），其性能提升主要源于对注射所致视网膜形变检测能力的增强。专家评估（n=4）证实了该方法的临床相关性及术中应用潜力。这些结果表明，结构化的iOCT声化技术可作为视网膜下注射实时手术引导中一种可行的辅助模态。