MRI Reconstruction Using Motion Curve Segmentation
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Solution Overview
Problem
Existing magnetic resonance imaging (MRI) techniques struggle to effectively reduce motion artifacts caused by subject movements during imaging, which can degrade image quality.
Innovation Solution
A method that involves acquiring time-dependent measurement data to determine a motion curve, which is used to separate the acquisition period into sub-periods based on defined motion conditions, allowing for the reconstruction of MRI images using data from specific sub-periods to minimize artifacts from movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If measurement data from the entire acquisition period is used for reconstruction, then the quantity of data available for image reconstruction is maximized, but motion artifacts are introduced that degrade image quality
Solution Approach 1:
The acquisition period is divided into multiple sub-periods based on motion curve analysis. Each sub-period represents a relatively stable motion state, allowing separate reconstruction without motion artifacts. This segmentation enables selective use of data from stable periods while excluding data from high-motion periods.
Solution Approach 2:
Different quality criteria are applied to different portions of the measurement data based on local motion characteristics. Data from sub-periods with low motion variability is selected for reconstruction, while data from sub-periods with high motion variability is discarded. This local quality assessment optimizes image quality by ensuring only suitable data contributes to each reconstructed image.
2Object-affected harmful factors
If the acquisition period is separated into multiple sub-periods to reduce motion artifacts, then image quality is improved, but the effective quantity of usable measurement data decreases
Solution Approach 1:
A motion curve is generated from the measurement data to provide feedback about motion characteristics throughout the acquisition period. This motion curve is analyzed to identify sub-periods with acceptable motion variability, enabling intelligent selection of data for reconstruction. The feedback mechanism ensures that data selection is based on actual motion conditions rather than arbitrary time divisions.
3Object-affected harmful factors
If existing techniques for periodic cardiac and respiratory movements are used, then some motion artifacts are reduced, but artifacts from non-periodic movements such as arm movements are not effectively addressed
Solution Approach 1:
The method transitions from static, predetermined motion correction approaches to a dynamic approach where the acquisition period is adaptively divided into sub-periods based on actual motion characteristics. The motion curve analysis enables real-time identification of stable and unstable periods, allowing the system to adapt to both periodic and non-periodic movements without requiring prior knowledge of motion patterns.
Data Source
AI summary
The invention relates to a method for reconstructing magnetic resonance images, to a magnetic resonance apparatus and to a computer program product. According to the method, measurement data is acquired in an acquisition period. The measurement data is used to determine a motion curve over the acquisition period. In addition, at least one magnetic resonance image is reconstructed. In this process, the acquisition period is separated into a plurality of sub-periods by one or more separating periods and/or separating times in which the motion curve satisfies a defined motion condition, and each magnetic resonance image is reconstructed using the measurement data from at least one sub-period.