Real-Time Medical Image Superposition via Spatial Marker Alignment
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Solution Overview
Problem
Current medical image visualization systems lack the ability to align and superimpose medical data with real-time patient images effectively, which can lead to increased procedure time and risk of errors during surgeries, as they do not provide a clear spatial understanding of anatomical structures without opening tissues.
Innovation Solution
A system comprising a processor and memory that identifies spatial markers in medical data-based images and real-time perceived images, superimposes and aligns them, using sensors to detect movement and update the positions of markers, allowing for real-time alignment and superimposition of medical data with perceived images, such as CAT, MRI, or ultrasound images, on devices like smartphones or wearable glasses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If medical data-based images are superimposed with real-time perceived images, then spatial understanding is improved, but device complexity increases
Solution Approach 1:
The patent uses sensors as intermediaries to detect anatomical landmarks in real-time images and automatically calculate transformation parameters. This mediator approach bridges the gap between medical data images and real-time perceived images, enabling accurate superposition without requiring complex manual alignment procedures from users.
Solution Approach 2:
The patent replaces manual mechanical alignment methods with automated computational algorithms. The system uses image processing algorithms and coordinate transformation mathematics to automatically align medical data images with real-time images, substituting complex manual mechanical adjustment with automated computational processes.
2Loss of time
If real-time image capture and processing is implemented, then procedure time is reduced, but use of energy increases
Solution Approach 1:
The patent implements periodic action by capturing real-time images at specific intervals rather than continuously, and by updating the superposition only when significant movement or changes are detected. This approach maintains real-time utility while reducing energy consumption by avoiding constant processing and capturing.
Solution Approach 2:
The system processes only the necessary portions of images - specifically focusing on detecting anatomical landmarks and transformation parameters rather than processing entire high-resolution images continuously. This partial processing approach reduces computational energy requirements while maintaining the real-time alignment functionality.
3Measurement precision
If multiple spatial markers are tracked and aligned, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex task of image alignment into distinct components: detecting individual anatomical landmarks, calculating transformation parameters for each landmark, and combining these transformations to achieve overall image alignment. This segmentation of the alignment process into manageable steps improves precision while controlling complexity.
Solution Approach 2:
The system uses universal mathematical transformation algorithms that can be applied to multiple different types of spatial markers and anatomical landmarks. This multi-functional approach allows the same core algorithm to handle various marker types and anatomical features, improving measurement precision across different scenarios without proportionally increasing system complexity.
Data Source
AI summary
Some systems include a memory, and a processor coupled to the memory, wherein the processor is configured to: identify one or more spatial markers in a medical data-based image of a patient, identify one or more spatial markers in a real-time perceived image of the patient, wherein the one or more spatial markers in the medical data-based image correspond to an anatomical feature of the patient and the one or more spatial markers in the real-time perceived image correspond to the anatomical feature of the patient, superimpose the medical data-based image of the patient with the real-time perceived image of the patient, and align the one or more spatial markers in the medical data-based image with the respective one or more spatial markers in the real-time perceived image.