Scanner Agnostic Image-Guided Biopsy Platform Employing Advanced Computer Vision and Computer Graphics to Enable Non-Invasive Registration and Dynamic Projection Mapping

PROJECT SUMMARY Accurate targeting during image-guided biopsy procedures is critical for the diagnosis and treatment of cancers such as lung, liver, and spine tumors. However, current CT-guided biopsy workflows rely on static 2D imaging and manual interpretation, which can lead to misdiagnosis, repeat procedures, and increased patient risk—especially in anatomically complex or mobile regions affected by respiration. This project proposes the continued development and validation of Skylight™, an innovative mixed-reality navigation platform that uses non-invasive adhesive fiducials (SkinDots™) and real-time projection mapping to guide biopsy tools with millimeter- scale accuracy. The long-term objective is to reduce diagnostic delays and procedural complications by enabling dynamic, spatially aware, and intuitive visualization of subsurface anatomy directly on the patient's body. This Phase II effort will advance Skylight’s capabilities by implementing and validating a real-time respiratory motion compensation algorithm using tracked external markers and preoperative imaging. Cadaveric models ventilated with programmable respirators will be used to simulate realistic anatomical motion and evaluate targeting accuracy across lung, liver, and spinal lesions. The specific aims are: (1) to develop and test software algorithms for tracking and compensating for respiratory motion; (2) to develop a cadaveric model with physiologic respiratory movement for system evaluation; and (3) to validate Skylight’s accuracy in guiding biopsies across common anatomical targets. The proposed system has the potential to significantly improve the safety, efficiency, and diagnostic yield of CT-guided procedures, aligning with NIH’s mission to improve health through innovative, patient-centered technologies.