Preoperative Dataset Correction for Vessel Deformation Alignment
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Solution Overview
Problem
Existing methods for correcting preoperative image datasets during medical procedures, such as EVARs, fail to accurately account for vessel deformation due to the arrangement of medical objects, leading to insufficient accuracy in deformation correction without depth information.
Innovation Solution
A method and system that utilize positioning information and entry angles of medical objects to generate a corrected dataset by minimizing deviations between preoperative and intraoperative datasets, incorporating sensors and processing units to adjust for spatial positioning and deformation caused by medical objects in the examination region.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If deformation correction is performed using only intraoperative X-ray projection images, then the correction process is simple, but the accuracy of deformation correction is insufficient
Solution Approach 1:
A medical object with a known spatial course serving as a reference intermediary is introduced into the vessel. This reference object enables accurate deformation correction by providing detectable markers whose positions can be tracked in both preoperative and intraoperative images, allowing the system to calculate actual vessel deformation caused by the medical object insertion.
Solution Approach 2:
The solution transitions from using only 2D intraoperative X-ray projection images to incorporating 3D spatial information from the medical object's known spatial course. By utilizing the predefined three-dimensional trajectory of the medical object, the system can perform accurate 3D deformation correction of the vessel, overcoming the limitation of 2D image-based correction methods.
2Quantity of substance
If a preoperative image dataset is registered and superimposed with intraoperative X-ray projection images, then contrast agent usage is reduced, but deformation of the vessel is not accurately represented
Solution Approach 1:
The system uses the detectable markers on the medical object to provide feedback about actual vessel deformation. By tracking the positions of these markers in intraoperative images and comparing them with the preoperative spatial course, the system calculates real-time deformation information and uses this feedback to dynamically correct the preoperative image dataset, ensuring accurate vessel representation.
Solution Approach 2:
The medical object with detectable markers is pre-configured with a known spatial course before the procedure. This preliminary setup allows the system to establish a reference trajectory that will be used throughout the procedure to detect and correct vessel deformation, enabling accurate image registration without requiring additional contrast agents.
3Device complexity
If no depth information is available from X-ray projection images, then the imaging process is simple, but the spatial positioning accuracy is insufficient
Solution Approach 1:
The system recovers 3D spatial information from 2D X-ray projection images by utilizing the known three-dimensional spatial course of the medical object. The detectable markers' positions in the 2D images are matched with their predetermined 3D coordinates, enabling the system to calculate depth information and perform accurate 3D spatial positioning of the vessel and medical object.
Data Source
AI summary
A method for providing a corrected dataset includes receiving a preoperative dataset having an image and/or a model of an examination region in an examination subject. Intraoperatively, a first part of a medical object is arranged in the examination region and a second part of the medical object is arranged outside the examination subject. Positioning information relating to a spatial positioning of the second part of the medical object is received. An entry angle of the medical object into the examination subject is determined using the positioning information. An intraoperative dataset having an image of the examination region is received. A conversion instruction is determined based on the entry angle of the medical object to minimize a deviation between the preoperative and the intraoperative dataset, and the corrected dataset is generated by applying the conversion instruction to the preoperative dataset. The corrected dataset is provided.