2D to 3D Registration Using Segmented Initialization and Refinement
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Solution Overview
Problem
Current methods for 2D to 3D registration in image-guided surgery are computationally intensive and often result in inaccurate registrations, taking several minutes to an hour to complete.
Innovation Solution
A method involving an initialization step to estimate the orientation and position of 3D image data, followed by a refinement step using image matching algorithms with similarity/cost measures to align two-dimensional fluoroscopic images with three-dimensional data, enhancing registration accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional 2D to 3D registration algorithms are used, then registration accuracy can be achieved, but the registration time becomes excessively long (several minutes to an hour)
Solution Approach 1:
The patent segments the registration process into two distinct phases: an initialization step that establishes a rough alignment using simplified methods, and a refinement step that applies computationally intensive algorithms only to reduce errors from the initial alignment. This segmentation allows the system to achieve accurate registration without requiring the full computational burden of traditional methods throughout the entire process.
Solution Approach 2:
The initialization step performs preliminary alignment actions before the refinement step begins. By pre-establishing a reasonable initial alignment using efficient algorithms, the system reduces the computational workload required in the subsequent refinement phase, thereby achieving accurate registration in less time than traditional methods that apply complex algorithms from scratch.
2Reliability
If computationally intensive registration algorithms are applied, then registration completeness is achieved, but the system becomes overly complex and difficult to implement
Solution Approach 1:
The patent divides the complex registration system into two manageable modules: an initialization module with simpler algorithms and a refinement module with more sophisticated algorithms. This segmentation makes the overall system easier to implement, debug, and maintain compared to a monolithic complex algorithm, while still achieving complete and reliable registration results.
Solution Approach 2:
By performing preliminary alignment in the initialization step, the system reduces the complexity of the refinement step. The refinement algorithm only needs to correct smaller residual errors rather than establishing alignment from scratch, simplifying the overall computational task and making the system more tractable for implementation.
3Manufacturing precision
If traditional registration methods are used, then comprehensive image alignment is achieved, but the surgical workflow efficiency decreases due to long processing times
Solution Approach 1:
The patent segments the image alignment process into initialization and refinement phases, enabling the system to achieve comprehensive alignment precision while significantly reducing the time required. This segmentation allows surgical workflows to maintain high precision requirements without sacrificing overall efficiency.
Solution Approach 2:
The initialization step performs preliminary image alignment actions that establish a good starting point before refinement begins. This preliminary action reduces the total processing time required to achieve the same level of alignment precision, thereby improving surgical workflow efficiency without compromising image alignment quality.
Data Source
AI summary
A method and apparatus for performing 2D to 3D registration includes an initialization step and a refinement step. The initialization step is directed to identifying an orientation and a position by knowing orientation information where data images are captured and by identifying centers of relevant bodies. The refinement step uses normalized mutual information and pattern intensity algorithms to register the 2D image to the 3D volume.