Surgical AR Guidance via Intra-Operative Optical Registration
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Solution Overview
Problem
Current augmented reality systems in surgery lack effective methods for accurately registering and guiding surgical procedures in real-time, particularly in complex operations like knee replacements, due to challenges in aligning virtual patient models with actual anatomical changes during the operation.
Innovation Solution
A method that utilizes optical scans and computer vision techniques to register virtual patient models with actual anatomical features, allowing for continuous alignment and adaptation during surgery by using intermediate features and spatial relationships, enabling real-time guidance and deviation tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If virtual patient models are used to guide surgical procedures, then surgical precision is improved, but alignment accuracy with actual anatomical changes deteriorates over time
Solution Approach 1:
The system continuously captures optical scans during surgery and compares actual anatomical features with the virtual patient model, providing real-time feedback to detect and correct deviations from the surgical plan. This closed-loop feedback mechanism maintains alignment accuracy despite anatomical changes throughout the procedure.
Solution Approach 2:
The registration system transitions from static pre-operative modeling to dynamic intra-operative tracking by continuously updating the alignment between virtual models and actual anatomy using real-time optical scans. This dynamic adaptation allows the system to accommodate anatomical changes such as tissue retraction, fluid accumulation, and surgical modifications.
2Measurement precision
If continuous optical scanning is performed during surgery, then real-time alignment is improved, but system complexity increases
Solution Approach 1:
The optical scanning system serves multiple functions: capturing anatomical geometry for registration, tracking surgical tool positions, monitoring anatomical changes, and providing real-time visual feedback. This multi-functionality reduces the need for separate specialized devices and simplifies the overall system architecture.
Solution Approach 2:
The system automatically performs feature detection, landmark identification, and registration updates without requiring manual intervention. The optical scanning and image processing occur autonomously, with the system self-correcting alignment deviations based on continuous comparison between virtual models and captured anatomy.
3Adaptability or versatility
If intermediate features are used for registration, then adaptability to anatomical changes is improved, but measurement difficulty increases
Solution Approach 1:
The system uses intermediate anatomical features such as skin landmarks, soft tissue contours, and bony prominences as mediators between the virtual patient model and the actual target anatomy. These intermediate features serve as reliable registration points that remain detectable even when primary surgical landmarks are obscured or modified during the procedure.
Solution Approach 2:
The optical scanning system utilizes color and texture information from captured images to identify and track anatomical features. By analyzing color variations in soft tissues, skin markings, and contrast-enhanced structures, the system can detect intermediate features that provide robust registration points throughout the surgical procedure.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides accurate, real-time alignment and adaptation of virtual patient models with actual anatomical changes, enhancing surgical precision and compliance with the surgical plan, thereby improving surgical outcomes and reducing errors.
Implementation Method 1
an optical sensor facing a surgical field occupied by the patient
Implementation Method 2
a first sequence of optical scans recorded by an optical sensor
Data Source
AI summary
One variation of a method includes: accessing a virtual patient model defining a target resected contour of a hard tissue of interest; after resection of the hard tissue of interest during a surgical operation, accessing an optical scan recorded by an optical sensor facing a surgical field occupied by a patient, detecting a set of features representing the patient in the optical scan, registering the virtual patient model to the hard tissue of interest in the surgical field based on the set of features, and detecting an actual resected contour of the hard tissue of interest in the optical scan; and calculating a spatial difference between the actual resected contour of the hard tissue of interest and the target resected contour of the hard tissue of interest represented in the virtual patient model registered to the hard tissue of interest in the surgical field.