Markerless 3D Image Alignment for Real-Time Medical Scan Registration
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Solution Overview
Problem
Existing image registration systems face challenges in achieving real-time, sub-millimeter precision without the use of markers, particularly in aligning 3D image data with medical scans, due to processing complexities and sensitivity to factors like illumination and camera pose.
Innovation Solution
A system and method for real-time multiple modality image alignment that transforms 3D point clouds to a different reference frame using transformation data structures, down-samples data points, and aligns them without markers, enabling precise registration of 3D images with medical scans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time image registration is performed without markers using complex processing algorithms, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the image registration process into distinct modules: point cloud generation from medical images, real-time 3D scene capture, feature extraction and matching, and transformation calculation. This modular segmentation allows each component to be optimized independently, reducing overall system complexity while maintaining sub-millimeter precision through specialized processing at each stage
Solution Approach 2:
The patent performs preliminary processing by pre-generating point clouds from medical images and pre-extracting features before real-time registration. This preliminary action prepares data structures and reduces computational load during actual registration operations, enabling high precision without requiring excessively complex real-time processing hardware
2Productivity
If real-time processing is implemented for multiple modality alignment, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent implements dynamic processing where the system adapts its computational approach based on input data characteristics and registration requirements. This includes dynamic feature selection, adaptive transformation methods, and flexible point cloud processing that adjusts complexity based on scene conditions, enabling real-time performance without uniformly high device complexity
Solution Approach 2:
The patent uses point cloud representations as simplified copies of the actual medical image data for real-time processing. These point cloud models capture essential geometric features while requiring minimal computational resources, enabling rapid registration operations without processing the full complexity of original medical images
3Ease of operation
If markerless registration is used to simplify operation, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent introduces point clouds as intermediary representations that bridge the gap between markerless operation and high precision alignment. These point clouds serve as mediators that capture precise geometric information from medical images and enable accurate feature matching without requiring physical markers, thus maintaining both ease of operation and measurement precision
4Manufacturing precision
If high precision transformation algorithms are applied, then manufacturing precision is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary computation of transformation parameters using pre-processed point cloud data and pre-extracted features. This preliminary action allows complex high-precision transformation algorithms to be executed on already-prepared data structures, significantly reducing actual registration time while maintaining accuracy
Solution Approach 2:
The patent implements iterative refinement where initial transformations are computed quickly using partial feature sets, then progressively refined with additional features. This partial action approach achieves high precision through multiple passes rather than requiring a single computationally intensive operation, reducing total processing time
Data Source
AI summary
A method for aligning multiple depth cameras in an environment based on image data can include accessing, by one or more processors, a plurality of first point cloud data points corresponding to a first pose relative to a subject and a plurality of second point cloud data points corresponding to a second pose relative to the subject. The method can include determining, by the one or more processors, a frame of reference for image data based on at least one of the first pose or the second pose. The method can include transforming, by the one or more processors, at least one of the plurality of first point cloud data points or the plurality of second point cloud data points to align with the frame of reference.