Nonrigid 2D/3D Registration for Coronary Artery Centerline Alignment
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Solution Overview
Problem
Current 2D/3D registration methods for guiding cardiac interventions face challenges in aligning preoperative 3D models with intraoperative fluoroscopic images due to nonlinear transformations and shape changes, leading to uncertainty and inaccuracies, especially in minimally invasive procedures like PCI for chronic total occlusions.
Innovation Solution
A nonrigid 2D/3D registration method that combines a global rigid or affine transformation with a fully nonrigid registration process, using distance maps and energy functionals to align a 3D coronary artery centerline model with multiple fluoroscopic images, allowing for local shape discrepancies and motion capture across frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a rigid transformation model is used for 2D/3D registration, then the registration process is simpler and faster, but the alignment precision deteriorates due to inability to account for shape changes
Solution Approach 1:
The patent transitions from a static rigid transformation model to a dynamic nonrigid transformation model that can adapt to shape changes. The nonrigid registration process allows the 3D model to deform locally to match the 2D fluoroscopic images, capturing the dynamic nature of cardiac structures during the procedure.
Solution Approach 2:
The patent changes the transformation parameters from fixed rigid parameters (rotation, translation) to variable nonrigid parameters that allow local deformation. This enables the model to accommodate breathing motions and other physiological variations while maintaining registration accuracy.
2Measurement precision
If a nonrigid transformation model is used for 2D/3D registration, then the alignment precision improves to account for shape changes, but the optimization complexity and computation time increase
Solution Approach 1:
The patent segments the 3D model into multiple control points or voxels that can be independently deformed. This segmentation allows the nonrigid transformation to be applied locally to different regions, simplifying the optimization process by breaking down the complex global deformation into smaller, manageable local adjustments.
Solution Approach 2:
The patent performs preliminary rigid or affine registration before the nonrigid registration. This preliminary alignment establishes a good initial pose, which simplifies the subsequent nonrigid optimization by reducing the search space and providing a favorable starting point for the iterative deformation process.
3Manufacturing precision
If 2D/3D registration is performed to reduce uncertainty in interventional imaging, then the surgical guidance accuracy improves, but the time required for registration increases
Solution Approach 1:
The patent implements continuous or near-continuous registration during the interventional procedure. By maintaining the 3D model updated in real-time with each fluoroscopic image, the system provides continuous surgical guidance without requiring repeated registration operations, thus reducing total time loss.
Solution Approach 2:
The patent applies partial nonrigid deformation only to the regions of interest rather than the entire 3D model. This selective approach reduces the computational burden while maintaining accuracy in the critical areas, thereby decreasing registration time without sacrificing surgical guidance precision.
Data Source
AI summary
A method for non-rigid registration of digital 3D coronary artery models with 2D fluoroscopic images during a cardiac intervention includes providing a digitized 3D centerline representation of a coronary artery tree that comprises a set of S segments composed of QS 3D control points, globally aligning the 3D centerline to at least two 2D fluoroscopic images, and non-rigidly registering the 3D centerline to the at least two 2D fluoroscopic images by minimizing an energy functional that includes a summation of square differences between reconstructed centerline points and registered centerline points, a summation of squared 3D registration vectors, a summation of squared derivative 3D registration vectors, and a myocardial branch energy. The non-rigid registration of the 3D centerline is represented as a set of 3D translation vectors rs,q that are applied to corresponding centerline points xs,q in a coordinate system of the 3D centerline.