SYSTEM AND METHOD FOR LIDAR-BASED ANATOMICAL MAPPING

Smart overview of the Invention

A system utilizes LIDAR imaging technology to create detailed surface maps of anatomical features, particularly the spine. By employing anatomical and artificial landmarks, this method allows for precise mapping of exposed anatomical surfaces. The generated surface maps can be integrated with previously acquired medical images, enhancing the identification of critical structures necessary for surgical procedures.

Integration with Augmented Reality

The system enables the visualization of important information, such as entry points for surgical instruments, through augmented reality overlays. This information can be displayed on devices like wearable eyeglasses, providing surgeons with real-time guidance during operations. The integration of LIDAR technology into wearable devices enhances the efficiency and accuracy of surgical interventions.

Device Configuration and Functionality

The apparatus comprises an electronic device equipped with orientation sensors and processors that simulate the positioning of medical devices relative to the body. It determines the desired insertion angles and provides notifications when the device aligns correctly for insertion. This functionality streamlines the process of placing medical devices accurately within a patient's anatomy.

Methods for Orientation and Insertion Point Identification

Methods described include simulating medical device orientations based on diagnostic representations of the anatomy. The system aligns instruments for precise positioning at identified entry points, ensuring that surgeons can operate without relying solely on their prior anatomical knowledge. This approach enhances the overall safety and effectiveness of surgical procedures.

Applications in Surgical Procedures

Particularly beneficial in spinal surgeries, the technology aids in identifying optimal entry points for tools like pedicle screws or intervertebral fusion devices. The system captures LIDAR data to create surface maps that correlate with existing diagnostic images, facilitating accurate and efficient surgical interventions. By automating entry point identification, it reduces the risk associated with manual placements during surgery.