Adaptive infrared patterns for microscopic surface reconstructions

Purpose: Multi-zoom microscopic surface reconstructions of operating sites, especially in ENT surgeries, would allow multimodal image fusion for determining the amount of resected tissue, for recognizing critical structures, and novel tools for intraoperative quality assurance. State-of-the-art three-dimensional model creation of the surgical scene is challenged by the surgical environment, illumination, and the homogeneous structures of skin, muscle, bones, etc., that lack invariant features for stereo reconstruction. Methods: An adaptive near-infrared pattern projector illuminates the surgical scene with optimized patterns to yield accurate dense multi-zoom stereoscopic surface reconstructions. The approach does not impact the clinical workflow. The new method is compared to state-of-the-art approaches and is validated by determining its reconstruction errors relative to a high-resolution 3D-reconstruction of CT data. Results: 200 surface reconstructions were generated for 5 zoom levels with 10 reconstructions for each object illumination method (standard operating room light, microscope light, random pattern and adaptive NIR pattern). For the adaptive pattern, the surface reconstruction errors ranged from 0.5 to 0.7 mm, as compared to 1–1.9 mm for the other approaches. The local reconstruction differences are visualized in heat maps. Conclusion: Adaptive near-infrared (NIR) pattern projection in microscopic surgery allows dense and accurate microscopic surface reconstructions for variable zoom levels of small and homogeneous surfaces. This could potentially aid in microscopic interventions at the lateral skull base and potentially open up new possibilities for combining quantitative intraoperative surface reconstructions with preoperative radiologic imagery.