2D-3D Medical Image Registration Convergence
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Solution Overview
Problem
Existing 2D-3D registration methods for robot-assisted surgery require a large number of iterations to converge, leading to time delays and potential failure to achieve accurate co-registration between preoperative 3D medical images and intraoperative 2D x-ray images.
Innovation Solution
The method initializes the computational configuration to ensure simulated and actual x-rays agree closely before starting iterations, reducing the number of iterations required for convergence and improving the accuracy of co-registration by storing 3D and 2D medical images, generating simulated 2D images, and comparing anatomical features until a match is reached.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If iterative 2D-3D registration methods are used to co-register preoperative 3D medical images with intraoperative 2D x-ray images, then accurate coordinate system transformation is achieved, but a large number of iterations are required causing time delays and potential convergence failure
Solution Approach 1:
The patent applies preliminary action by performing an initial configuration step before the iterative registration process. The system initializes the computational configuration to ensure simulated and actual x-rays agree closely before iterations begin, using pre-stored 3D and 2D medical images to establish a good starting point. This preliminary setup reduces the number of iterations needed for convergence while maintaining registration accuracy.
2Measurement precision
If iterative 2D-3D registration methods are used to co-register preoperative 3D medical images with intraoperative 2D x-ray images, then accurate coordinate system transformation is achieved, but the incidence of convergence failure increases
Solution Approach 1:
The patent applies preliminary action by performing an initial configuration step before the iterative registration process. The system initializes the computational configuration to ensure simulated and actual x-rays agree closely before iterations begin, using pre-stored 3D and 2D medical images to establish a good starting point. This preliminary setup reduces the number of iterations needed for convergence while maintaining registration accuracy.
Solution Approach 2:
The patent applies beforehand cushioning by storing both 3D and 2D medical images in advance and initializing the computational configuration to ensure simulated and actual x-rays agree closely before iterations begin. This preparatory measure creates a cushion against potential convergence failures by establishing a robust starting configuration that is closer to the final solution, thereby reducing the risk of iteration failure.
3Measurement precision
If a large number of iterations are performed in 2D-3D registration, then registration accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent applies preliminary action by performing an initial configuration step before the iterative registration process. The system initializes the computational configuration to ensure simulated and actual x-rays agree closely before iterations begin, using pre-stored 3D and 2D medical images to establish a good starting point. This preliminary setup reduces the number of iterations needed for convergence while maintaining registration accuracy.
Data Source
AI summary
A method for registration of digital medical images is provided. The method includes the step of storing a 3D digital medical image having a 3D anatomical feature and a first coordinate system and storing a 2D digital medical image having a 2D anatomical feature and a second coordinate system. The method further includes the steps of storing a placement of a digital medical object on the 3D digital medical image and the 2D digital medical image and generating a simulated 2D digital medical image from the 3D digital medical image, wherein the simulated 2D digital medical image comprises a simulated 2D anatomical feature corresponding to the 3D anatomical feature. The 2D anatomical feature is compared with the simulated 2D anatomical feature until a match is reached and a registration of the first coordinate system with the second coordinate system based on the match is determined.