Four-Dimensional DSA Reconstruction for Vascular Dynamics
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Solution Overview
Problem
Current Digital Subtraction Angiography (DSA) methods primarily provide two-dimensional time-resolved information, leading to challenges in interpreting three-dimensional vascular anatomy due to vessel overlap and occlusions, and existing 3D reconstructions lack temporal dynamics, resulting in inaccurate flow patterns and increased radiation exposure.
Innovation Solution
A framework for refined four-dimensional reconstruction of time-varying data to generate a four-dimensional DSA dataset, allowing for the extraction and refinement of a volume of interest, enabling a zoomed-in four-dimensional representation with improved spatial resolution and accurate hemodynamic analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If 2D DSA images are used to visualize vascular filling, then time-resolved information is available, but vessel overlap and occlusions occur leading to compromised image information
Solution Approach 1:
The patent transitions from 2D DSA images to 4D DSA datasets by adding spatial (3D volumetric) and temporal (time-resolved) dimensions. This allows visualization of vascular filling dynamics without vessel overlap or occlusion issues that plague 2D imaging, as the 4D representation shows vessels in three-dimensional space across multiple time points.
Solution Approach 2:
The patent segments the 4D DSA dataset into a volume of interest (VOI) and background, then extracts and refines only the relevant vascular structures within the VOI. This segmentation allows detailed analysis of specific vascular regions while eliminating interfering structures outside the VOI, improving both information retention and interpretation accuracy.
2Shape
If 3D reconstruction techniques are applied to 2D DSA data, then volumetric visualization is achieved, but temporal dynamics are lost resulting in static reconstructions
Solution Approach 1:
The patent merges 3D volumetric reconstruction with 2D time-resolved DSA data to create a 4D dataset that simultaneously provides volumetric visualization and temporal dynamics. This combination allows observation of vascular filling changes over time within a three-dimensional context, preserving both spatial and temporal information.
Solution Approach 2:
The patent creates a dynamic 4D representation where vascular structures can be visualized changing over time. The system allows interactive exploration of temporal changes in vascular filling, enabling observation of hemodynamic patterns and vascular morphology evolution that static 3D reconstructions cannot capture.
3Volume of stationary object
If traditional 3D reconstruction methods are used, then volumetric data is obtained, but increased radiation exposure occurs due to additional acquisition sequences
Solution Approach 1:
The patent extracts only the necessary volumetric information from the 4D DSA dataset by identifying and isolating the volume of interest containing relevant vascular structures. This extraction approach obtains volumetric data without requiring additional radiation acquisition sequences, as the 3D information is derived from existing 2D time-resolved images through computational reconstruction.
Solution Approach 2:
The patent creates a computational copy of the volumetric vascular structure from 2D projection images using 4D reconstruction algorithms. This virtual 3D model serves as an accurate representation of vascular anatomy and dynamics without requiring physical 3D imaging acquisitions, thereby avoiding additional radiation exposure while preserving volumetric information.
4Device complexity
If simplified models of blood flow and physiology are used, then reconstruction is simplified, but flow accuracy decreases leading to deviations from real flow patterns
Solution Approach 1:
The patent employs iterative reconstruction algorithms that use feedback from the 2D time-resolved DSA images to continuously refine the 4D volumetric model. The system adjusts the reconstruction based on actual observed vascular filling patterns, ensuring high accuracy in flow measurement while maintaining computational feasibility through automated iterative optimization.
Data Source
AI summary
Systems and methods are provided for refined data reconstruction. In accordance with one aspect, the framework performs a first four-dimensional reconstruction of time-varying data to generate a four-dimensional Digital Subtraction Angiography (DSA) dataset of an object of interest. The framework extracts a volume of interest from the four-dimensional DSA dataset to generate a volume array. The volume of interest may be refined based on the volume array to generate a refined dataset. A second four-dimensional reconstruction may then be performed based on the refined dataset to generate a zoomed-in four-dimensional representation of the volume of interest.