MRI Motion Signal Acquisition Using Eigenvector Synthesis
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Solution Overview
Problem
Magnetic resonance imaging (MRI) techniques face challenges in acquiring clear images due to unavoidable motions such as respiration and heartbeat, which introduce motion artifacts. Existing methods for detecting these motions are complex and require additional sensors, making them cumbersome for both operators and patients.
Innovation Solution
A method and apparatus for acquiring target motion signals in MRI scans using multi-channel complex signals. This involves modulating a high-frequency signal with target motion signals, acquiring these signals through multiple channels, and using a motion signal synthesis vector to remove interference. The synthesis vector is derived from an eigenvector of a motion signal correlation matrix, calculated based on data with and without external interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If additional sensors are used to detect motion, then motion detection accuracy is improved, but device complexity increases
Solution Approach 1:
The patent applies self-service by enabling the MRI system to detect motion signals using its existing multi-channel receiver and signal processing capabilities. The system processes signals from the MRI scanner's own channels to extract motion information, eliminating the need for separate motion detection sensors. This resolves the contradiction by maintaining measurement precision while reducing device complexity through self-service functionality.
Solution Approach 2:
The patent implements universality by making the multi-channel receiver serve dual purposes: both receiving MRI signals and detecting motion signals. The same hardware infrastructure is utilized for both imaging and motion detection, thereby avoiding additional device complexity while maintaining accurate motion detection capability through multi-functional use of existing components.
2Ease of manufacture
If motion signals are not removed, then image acquisition is simple, but image quality deteriorates due to motion artifacts
Solution Approach 1:
The patent applies the extraction principle by isolating and removing motion signal components from the received signals. Through signal processing techniques, the motion signals are extracted and separated from the MRI signals, allowing for the removal of motion artifacts while maintaining image quality. This resolves the contradiction by enabling simple acquisition procedures while producing high-quality images free from motion artifacts.
Solution Approach 2:
The patent converts the harmful motion artifacts into beneficial information by detecting motion signals and using them to guide the image acquisition process. The motion information, which initially degrades image quality, is transformed into a control mechanism that triggers acquisition during optimal moments (such as expiration or cardiac diastole), thereby improving image quality while maintaining operational simplicity.
3Manufacturing precision
If acquisition time is extended to reduce noise, then image quality is improved, but motion artifacts increase
Solution Approach 1:
The patent applies preliminary action by detecting motion signals before image acquisition and using this information to determine the optimal timing for acquisition. The system anticipates motion patterns (such as respiration or heartbeat cycles) and schedules acquisition during phases of minimal motion, thereby reducing motion artifacts while maintaining sufficient acquisition time for high image quality without noise.
Solution Approach 2:
The patent implements feedback by continuously monitoring motion signals during the acquisition process and using this feedback to adjust acquisition timing and parameters. The motion detection provides real-time information that feeds back into the acquisition control system, allowing dynamic optimization of acquisition timing to minimize motion artifacts while ensuring sufficient data collection time for high-quality images.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method effectively removes external interference and isolates target motion signals, such as respiration and heartbeat, allowing for improved image acquisition with reduced motion artifacts. This results in higher-quality diagnostic images without the need for additional sensors, simplifying the imaging process.
Implementation Method 1
Magnetic resonance imaging (MRI) is a technique that uses magnetic resonance phenomena for imaging. The principle of magnetic resonance imaging mainly comprises: Nuclei containing an odd number of protons, for example, hydrogen nuclei widely existing in a human body, the protons thereof are in a spin motion, like small magnets... If an external magnetic field is applied, the small magnets will be rearranged according to magnetic lines of force of the external magnetic field... The nuclei only have a longitudinal magnetization component that has both a direction and an amplitude. Nuclei in the external magnetic field are excited by radio frequency (RF) pulses at a specific frequency such that the axes of spin of the nuclei deviate from the positive longitudinal axis or the negative longitudinal axis so as to produce resonance, which is the magnetic resonance phenomenon.
Implementation Method 2
acquiring multi-channel complex signals received by multiple channels, where each of the multi-channel complex signals is a signal received after a high-frequency signal is modulated by at least one target motion signal of a scanned object in magnetic resonance scanning
Data Source
AI summary
Method acquiring a target physiological motion signal, including: acquiring multi-channel complex signals received by multiple channels; and acquiring, from the multi-channel complex signals using a motion signal synthesis vector corresponding to a target motion signal, a target motion complex signal with interference removed; acquiring data received by multiple channels, the data including data without external interference in a first sub-period and data with external interference in a second sub-period; acquiring an external interference suppression matrix based on the data, and acquiring external interference suppression data based on the data without external interference or the data with external interference and the external interference suppression matrix; acquiring a motion signal correlation matrix of the target motion signal in frequency domain based on a frequency range of the target motion signal; and using, as a motion signal synthesis vector, an eigenvector acquired according to an eigenvalue of the motion signal correlation matrix.