AR Overlay Registration for Surgical Anatomy Alignment
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Solution Overview
Problem
Minimally invasive surgical procedures face challenges in aligning endoscope images with pre- or intraoperative imaging modalities to visualize both surface and subsurface features of patient anatomy, making it difficult for surgeons to obtain a complete view of relevant anatomy during surgeries.
Innovation Solution
An augmented reality imaging system is integrated with computer-assisted surgical systems like the da Vinci Surgical System, allowing for the registration and overlay of preoperative or intraoperative tomographic models onto real-time endoscope images, enabling the creation of composite images that include both surface and subsurface anatomical structures, with kinematic tracking to maintain alignment during surgical maneuvers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If endoscope images are combined with preoperative or intraoperative imaging modalities to visualize subsurface features, then the completeness of anatomical view is improved, but the difficulty of alignment and registration between images increases
Solution Approach 1:
The patent uses fiducial markers as intermediary elements that are placed on the patient's anatomy and detected by both the endoscope and preoperative imaging modalities. These markers serve as common reference points that facilitate the registration and alignment between different imaging systems, solving the difficulty of aligning endoscope images with subsurface anatomical models.
2Measurement precision
If augmented reality imaging systems overlay computer models onto endoscope images, then surgical precision is improved, but the complexity of the system increases
Solution Approach 1:
The patent merges the endoscope imaging system with preoperative imaging data and computer-assisted surgical systems into an integrated augmented reality platform. By combining these separate systems into a unified workflow that shares common coordinate systems and registration mechanisms, the patent reduces overall system complexity while maintaining high surgical precision.
Solution Approach 2:
The augmented reality imaging system is designed to work with multiple imaging modalities (endoscope, CT, MRI, fluoroscopy) and can be integrated with various computer-assisted surgical systems. This multi-functional design allows a single system to perform multiple functions, reducing the need for separate specialized systems and thereby reducing overall complexity.
3Measurement precision
If real-time composite images are generated by overlaying computer models on endoscope images, then surgical accuracy is improved, but the computational requirements and processing time increase
Solution Approach 1:
The patent performs registration and alignment calculations in advance during the setup phase, establishing transformation matrices and coordinate system relationships before the actual surgical procedure begins. This preliminary computation reduces the real-time processing burden during surgery, allowing composite images to be generated quickly without compromising surgical accuracy.
Data Source
AI summary
A system and method for registration and coordinated manipulation of augmented reality image components includes registering a model of patient anatomy to a first image of the patient anatomy captured using an imaging device to determine a baseline relationship between the model and the first image, tracking movement of a computer-assisted device used to manipulate the imaging device, updating the baseline relationship based on the tracked movement, and generating a composite image by overlaying the model on a second image of the patient anatomy according to the updated relationship. In some embodiments, the model is semi-transparent. In some embodiments, registering the model to the first image includes adjusting the model relative to the first image based on one or more inputs received from a user and generating a model to image transformation based on the adjustments to the model. The model to image transformation captures the baseline relationship.