3D Interventional Device Shape Prediction Within Vascular Geometry
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Solution Overview
Problem
Existing interventional medical procedures face challenges in accurately determining the three-dimensional shape of devices within vascular regions due to poor visibility of soft tissues in X-ray imaging, necessitating multiple two-dimensional projections that increase radiation dose and may be hindered by patient positioning, and pre-operative CT images are limited in guiding these procedures.
Innovation Solution
A computer-implemented method using a neural network trained with volumetric and X-ray image data to predict the three-dimensional shape of interventional devices within vascular regions, constrained by volumetric image data, utilizing techniques like supervised learning and loss functions to ensure accuracy and adherence to vascular geometry.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple two-dimensional X-ray images are taken from different projection angles to confirm device position, then measurement precision is improved, but patient radiation dose increases
Solution Approach 1:
The patent transforms multiple 2D X-ray images into a 3D representation of the interventional device by training a neural network to predict the device's three-dimensional shape and position from multiple 2D projection images. This dimensional transformation allows accurate device localization without requiring multiple separate 2D images to be interpreted individually, thereby reducing radiation exposure while maintaining measurement precision.
Solution Approach 2:
The patent creates a virtual 3D copy of the interventional device based on 2D X-ray image data. The neural network generates a predicted 3D shape that represents the actual device position and orientation, allowing physicians to visualize device location in three dimensions without acquiring multiple additional 2D images at different angles, thus reducing radiation dose while improving position confirmation accuracy.
2Measurement precision
If multiple two-dimensional X-ray images are taken from different projection angles to confirm device position, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent introduces a neural network as an intermediary that processes multiple 2D X-ray images and automatically generates a 3D reconstruction of the interventional device. This intermediary system performs the complex task of integrating multiple 2D projections and inferring 3D geometry, simplifying the overall workflow by automating the image processing and eliminating the need for manual interpretation of multiple 2D images from different angles.
Solution Approach 2:
The patent creates a virtual 3D copy of the interventional device based on 2D X-ray image data. The neural network generates a predicted 3D shape that represents the actual device position and orientation, allowing physicians to visualize device location in three dimensions without acquiring multiple additional 2D images at different angles, thus reducing radiation dose while improving position confirmation accuracy.
3Measurement precision
If contrast agents are used to improve vasculature visibility, then measurement precision is improved, but harmful factors increase due to adverse reactions
Solution Approach 1:
The patent creates a virtual 3D copy of the interventional device based on 2D X-ray image data. The neural network generates a predicted 3D shape that represents the actual device position and orientation, allowing physicians to visualize device location in three dimensions without acquiring multiple additional 2D images at different angles, thus reducing radiation dose while improving position confirmation accuracy.
Solution Approach 2:
The patent replaces the mechanical/chemical approach of using contrast agents to enhance vasculature visibility with a computational approach. The neural network processes existing X-ray images to reconstruct 3D device geometry and position, substituting the need for contrast agents with an algorithmic solution that achieves improved visualization without introducing harmful substances into the patient's body.
4Measurement precision
If desired projection angles are obtained to improve device visualization, then measurement precision is improved, but ease of operation decreases due to patient positioning constraints
Solution Approach 1:
The patent transforms multiple 2D X-ray images into a 3D representation of the interventional device by training a neural network to predict the device's three-dimensional shape and position from multiple 2D projection images. This dimensional transformation allows accurate device localization without requiring multiple separate 2D images to be interpreted individually, thereby reducing radiation exposure while maintaining measurement precision.
Solution Approach 2:
The patent introduces a neural network as an intermediary that processes multiple 2D X-ray images and automatically generates a 3D reconstruction of the interventional device. This intermediary system performs the complex task of integrating multiple 2D projections and inferring 3D geometry, simplifying the overall workflow by automating the image processing and eliminating the need for manual interpretation of multiple 2D images from different angles.
Data Source
AI summary
A computer-implemented method of providing a neural network for predicting a three-dimensional shape of an interventional device disposed within a vascular region, includes: training (S140) a neural network (140) to predict, from received X-ray image data (120) and received volumetric image data (110), a three-dimensional shape of the interventional device constrained by the vascular region (150). The training includes constraining the adjusting of parameters of the neural network such that the three-dimensional shape of the interventional device predicted by the neural network (150) fits within the three-dimensional shape of the vascular region represented by the received volumetric image data (110).