MRA Frame Subtraction for Cross-Sectional Perfusion Imaging
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Solution Overview
Problem
Existing methods for evaluating collateral circulation in cerebral infarction patients using dynamic contrast-enhanced MRA images are inaccurate due to the inability to provide sectional images, leading to decreased accuracy in diagnosing conditions like cerebral infarction.
Innovation Solution
A method and system for obtaining cross-sectional images by determining target and reference image frames based on time-intensity curves from MRA images, allowing for the calculation of subtraction data to identify perfusion characteristics without additional contrast medium use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If dynamic contrast-enhanced MRA is performed to evaluate collateral circulation, then imaging speed is improved, but measurement precision deteriorates due to inability to provide sectional images
Solution Approach 1:
The patent segments the MRA imaging process into multiple time points (peak arterial phase, peak venous phase, and intermediate phase) and reconstructs sectional images for each phase. This allows evaluation of collateral circulation at different stages of contrast medium flow, providing both speed (through efficient multi-phase acquisition) and precision (through phase-specific sectional analysis that was previously unavailable).
Solution Approach 2:
The patent transitions from conventional 3D volumetric MRA data to 2D sectional images by reconstructing cross-sectional views at specific time points. This dimensional transformation enables precise visualization of collateral circulation patterns in anatomical planes, resolving the contradiction by providing both the speed of 3D acquisition and the precision of 2D sectional evaluation.
2Reliability
If additional contrast medium is used for perfusion imaging after MRA, then diagnostic capability is improved, but harmful factors increase due to renal function impairment
Solution Approach 1:
The patent makes the single contrast medium administration serve multiple diagnostic functions by acquiring MRA images at multiple time points (arterial phase, venous phase, and intermediate phase) and reconstructing both angiographic and perfusion images from the same dataset. This multi-functional approach eliminates the need for additional contrast medium while maintaining comprehensive diagnostic capability for both vascular anatomy and tissue perfusion.
Solution Approach 2:
The patent changes the temporal parameter of image acquisition by capturing MRA data at multiple distinct time points following contrast medium injection. By analyzing signal intensity variations over time at the same location, the system extracts both vascular morphology (from early arterial phase) and perfusion characteristics (from later phases) without requiring additional contrast material, thus improving reliability while reducing harmful effects.
3Measurement precision
If temporary carotid artery contrast technology is used for collateral circulation evaluation, then measurement precision is improved, but device complexity and operation difficulty increase due to invasive procedure
Solution Approach 1:
The patent enables the imaging system to automatically perform collateral circulation evaluation by reconstructing sectional images from routine dynamic contrast-enhanced MRA data. The system self-processes the acquired volumetric data through multi-phase reconstruction algorithms, eliminating the need for invasive temporary carotid artery catheterization while maintaining measurement precision through automated temporal and spatial analysis of the contrast flow patterns.
Data Source
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AI summary
Disclosed are a method and a system for acquiring an additional image, for identifying perfusion, by using a contrast-enhanced MRA image. According to one embodiment of the present invention, the method comprises the steps of: (S10) acquiring an MRA image, which comprises a plurality of image frames, for an object by using MRI equipment; (S20) determining at least one target image frame among the image frames; (S30) determining at least one reference image frame, which corresponds to the at least one target image frame, among the image frames; (S40) calculating at least one piece of subtracted data by subtracting the data of the corresponding reference image frame from the data of each target image frame; and (S50) acquiring at least one cross-sectional image for an area-of-interest of the object on the basis of each piece of the subtracted data.