Medical Image Reconstruction With Facial Tracking Alignment
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Solution Overview
Problem
Current medical image reconstruction methods for cranial region treatments require expensive fiducial markers and are prone to alignment failures due to patient movement, necessitating improved registration processes.
Innovation Solution
A method involving facial recognition technology and 3D scanning to create a patient-specific image reconstructed model, overlaying it with real-time camera images, and using hand and instrumentation angle tracking for precise alignment and trajectory guidance during medical procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fiducial markers and traditional registration processes are used for medical image reconstruction, then alignment precision can be achieved, but the equipment becomes expensive and occupies large physical space
Solution Approach 1:
The patent uses virtual fiducial markers generated from 3D facial recognition models instead of physical fiducial markers. The system creates a digital representation of the patient's face with virtual markers that can be overlaid on real-time camera images, eliminating the need for expensive physical registration equipment while maintaining alignment precision through computational methods
Solution Approach 2:
The patent replaces mechanical registration systems with a computational approach. Instead of using physical fiducial markers and mechanical alignment devices, the system uses facial recognition algorithms to automatically identify anatomical landmarks and calculate transformations, substituting mechanical processes with digital image processing and machine learning models
2Measurement precision
If traditional registration processes are used for medical image reconstruction, then initial alignment can be achieved, but alignment fails when the patient's face moves
Solution Approach 1:
The patent implements a dynamic registration system that continuously updates the alignment as the patient moves. The system processes real-time camera images, detects facial landmarks, and recalculates the transformation matrix moment-by-moment, allowing the virtual fiducial markers to track and adapt to facial movements automatically, maintaining alignment accuracy throughout the procedure
Solution Approach 2:
The system uses real-time feedback from camera images to continuously monitor and adjust the alignment. Facial recognition algorithms detect changes in facial position and appearance, providing feedback to the registration system which then updates the virtual marker positions and transformation parameters to maintain accurate alignment despite patient movement
3Device complexity
If manual alignment methods are used for medical image reconstruction, then equipment costs are reduced, but the process takes significant time
Solution Approach 1:
The system performs automatic self-alignment without requiring manual intervention. The facial recognition model automatically detects facial landmarks, calculates transformations, and registers the virtual fiducial markers to the patient's anatomy in real-time, eliminating the need for manual registration procedures and significantly reducing the time required compared to manual methods
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
Facilitates accurate and efficient medical procedures by maintaining alignment despite patient movement, reducing equipment costs, and enhancing procedural accuracy and speed.
Implementation Method 1
The mapping points may include a facemask, which may be determined by a user of the display device and/or identified using a light detection and ranging (LIDAR) device
Data Source
AI summary
The disclosure relates to a method including receiving a first set of images, the images being scans from one or more medical instruments; creating a patient-specific image reconstructed model based on the first set of images; defining mapping points on the reconstructed model; receiving a second set of images; defining patient recognition mapping points on the second set of images; overlaying the reconstructed model and the second set of images based on the mapping points and the recognition mapping points; and displaying the overlaid model aligned with the second set of images.