Vision-Based Patient Carrier Positioning via Operator Activity Analysis
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Solution Overview
Problem
Existing medical imaging systems face challenges in accurately and consistently positioning patient carriers relative to the diagnostic imaging system, especially during preparation when the target anatomy may be obstructed or not directly visible.
Innovation Solution
A positioning control system that utilizes a camera system to acquire image information and an analysis module to compute the spatio-temporal pattern of operator activities, allowing the system to derive the location of the target anatomy and accurately position the patient carrier within the imaging zone.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional positioning methods (e.g., light visors, manual alignment) are used, then the patient carrier can be positioned relative to the imaging system, but the positioning accuracy and consistency deteriorate when the target anatomy is obstructed or not directly visible
Solution Approach 1:
The patent replaces traditional mechanical/optical alignment systems (light visors, manual positioning) with a vision-based system using cameras and machine learning. The camera system captures images of the patient and operator activities, and an analysis module processes these images to automatically determine the target anatomy location and position the patient carrier, eliminating the need for direct visual line-of-sight alignment tools.
Solution Approach 2:
The system creates a visual copy (image) of the patient and operator activities through the camera system. Instead of requiring direct visual observation or physical alignment tools, the system captures images and processes them computationally to derive positioning information, allowing accurate positioning even when the target is not directly visible to human operators.
2Ease of operation
If cumbersome auxiliary equipment like light visors is used for positioning, then the patient carrier can be aligned with the imaging zone, but the device complexity and ease of operation worsen
Solution Approach 1:
The system enables self-service positioning by automatically analyzing operator activities and patient anatomy through camera images, then autonomously computing and executing the positioning of the patient carrier. The system serves itself by using visual information to guide its own positioning without requiring external alignment tools or complex manual procedures.
Solution Approach 2:
The patent extracts the essential positioning function from complex auxiliary equipment (light visors, manual alignment tools) and implements it through a simplified vision-based system. By removing the need for these cumbersome devices and replacing them with image capture and computational analysis, the system achieves positioning with greater ease of operation and reduced device complexity.
3Productivity
If manual positioning methods are used, then the patient carrier can be moved to the imaging zone, but the positioning consistency and time efficiency deteriorate
Solution Approach 1:
The system implements continuous feedback by capturing real-time images of operator activities and patient positioning, analyzing these images to determine the current position and orientation, and using this information to automatically adjust the patient carrier positioning. This closed-loop feedback mechanism ensures both speed and consistency by continuously monitoring and correcting positioning based on visual feedback.
Solution Approach 2:
The system performs preliminary analysis of operator activities and patient anatomy through camera imaging before final positioning is executed. By pre-processing visual information to identify the target anatomy and calculate positioning parameters in advance, the system can execute rapid, consistent positioning without manual measurement or adjustment during the actual positioning phase.
Data Source
AI summary
A positioning control system for a patient carrier comprises a camera system to acquire image information from a detection range. An analysis module configured to access the acquired image information from the detection range and compute operator-activity within the detection range from the acquired image information. The operator-activity representing a spatio-temporal pattern of activities of an operator in the detection range. From the operator activity compute a location of a target anatomy that is selected to be imaged. The location of the target anatomy that is to be imaged can be derived from the spatio-temporal activity pattern of the operator during the preparation of the patient to be examined.