Vessel Segment Reconstruction Using Adaptive Angiographic Geometry
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Solution Overview
Problem
Current methods for reconstructing vessel segments from angiographic projection images often result in reconstruction errors due to suboptimal acquisition geometries, leading to uncertainties and inefficiencies in diagnostic evaluations, particularly in assessing stenosis in coronary arteries, as they require additional imaging and increased radiation exposure.
Innovation Solution
A method that automatically evaluates the suitability of acquired angiographic projection images using three-dimensional preliminary information to determine if additional images are needed, optimizing the acquisition geometries and reducing the number of required images, thereby improving reconstruction quality and reducing radiation exposure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If additional angiographic projection images are acquired to improve reconstruction quality, then manufacturing precision of the reconstruction data set is improved, but loss of energy increases due to additional radiation exposure
Solution Approach 1:
The system performs preliminary evaluation of acquired projection images using three-dimensional preliminary information before final reconstruction. This preliminary action identifies which images meet quality criteria, preventing unnecessary acquisition of additional images and reducing radiation exposure while maintaining reconstruction quality.
Solution Approach 2:
The system uses feedback from the preliminary evaluation to determine whether additional projection images are needed. The evaluation measure based on three-dimensional preliminary information provides feedback on image suitability, allowing the system to optimize the set of acquired images and avoid unnecessary radiation exposure.
2Device complexity
If manually selected angiographic projection images are used for reconstruction, then device complexity is reduced, but manufacturing precision of the reconstruction data set deteriorates due to suboptimal acquisition geometries
Solution Approach 1:
The system performs automatic evaluation of projection image suitability using three-dimensional preliminary information, eliminating the need for manual selection by medical staff. This self-service approach automatically identifies optimal acquisition geometries and ensures reconstruction quality without increasing operational complexity.
Solution Approach 2:
The system changes the evaluation parameters by using three-dimensional preliminary information to assess projection image suitability. This parameter change enables automatic identification of optimal acquisition geometries, improving reconstruction quality without requiring complex manual intervention.
3Manufacturing precision
If a larger set of projection images is acquired to ensure sufficient angular coverage, then manufacturing precision of the reconstruction data set is improved, but loss of time increases due to longer examination duration
Solution Approach 1:
The system performs preliminary evaluation to determine the minimum sufficient set of projection images needed for accurate reconstruction. This preliminary action identifies which images provide adequate angular coverage, reducing the total number of images required and shortening examination duration while maintaining reconstruction quality.
Solution Approach 2:
The system uses partial action by acquiring only the necessary subset of projection images rather than a complete set. The preliminary evaluation identifies the minimum sufficient angular coverage needed, avoiding excessive image acquisition and reducing examination time while maintaining reconstruction quality.
4Ease of operation
If retrospective manual selection of projection images is performed, then ease of operation is improved, but manufacturing precision deteriorates due to inadequate evaluation of acquisition geometry suitability
Solution Approach 1:
The system automatically evaluates projection image suitability using three-dimensional preliminary information, replacing manual selection with an automated self-service process. This maintains ease of operation while significantly improving reconstruction quality through objective geometric evaluation.
Solution Approach 2:
The system replaces the mechanical process of manual image selection with an automated computational evaluation system. The three-dimensional preliminary information enables automatic assessment of acquisition geometry suitability, substituting manual judgment with objective algorithms to improve reconstruction quality.
Data Source
AI summary
A method and system are provided for at least symbolically reconstructing a reconstruction data set of at least one vessel segment in a vessel tree of a patient. Input data for the reconstruction comprises at least two two-dimensional angiographic projection images taken in different acquisition geometries. At least one first angiographic projection image showing the vessel segment is acquired. An evaluation measure is automatically determined for each first angiographic projection image using three-dimensional preliminary information for the vessel segment. The evaluation measure describes the suitability of the at least one angiographic projection image for reconstructing the reconstruction data set. When a quality criterion evaluating the evaluation measure is not fulfilled, at least one additional acquisition geometry is determined using the three-dimensional preliminary information and/or the evaluation measure. In each additional acquisition geometry, at least one second angiographic projection image is acquired. The reconstruction data set is reconstructed from the at least one second angiographic projection image and/or at least one of the at least one first angiographic projection image fulfilling a suitability criterion evaluating the evaluation measure.