MRI Navigator Signal Analysis for Respiration Artifact Reduction
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Solution Overview
Problem
Magnetic resonance imaging (MRI) techniques using non-selective RF pulses excite wide areas, leading to noise issues and difficulty in obtaining high-quality respiration signals due to signal components from irrelevant body parts, and challenges in identifying suitable coil elements for respiratory motion detection.
Innovation Solution
A magnetic resonance imaging apparatus that performs a navigator sequence using non-selective RF pulses to generate MR signals, analyzes feature quantities of these signals across multiple coil elements, transforms the data into frequency spectra, and selects suitable coil elements based on peak reflections of respiratory motion for accurate signal determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If a non-selective RF pulse is used for excitation, then noise during imaging is reduced, but signal components from irrelevant body parts are included making it difficult to obtain high-quality respiration signals
Solution Approach 1:
The invention segments the signal processing by analyzing feature quantities from multiple coil elements separately, transforming each into frequency spectra, and selecting only the coil element that provides the respiration signal with the highest amplitude. This segmentation allows the system to maintain the low-noise advantage of non-selective RF pulses while eliminating contamination from irrelevant body parts through selective coil element choice.
2Device complexity
If coil elements are selected based on fixed positional assumptions, then the identification process is simplified, but accuracy decreases due to subject-to-subject variation in body part positions
Solution Approach 1:
The invention implements a self-service approach where the system automatically determines the optimal coil element by analyzing feature quantities and frequency spectra from all coil elements without requiring manual intervention or fixed positional assumptions. The process autonomously identifies the coil element that yields the highest respiration signal amplitude, adapting to each subject's unique anatomy while maintaining high accuracy.
3Measurement precision
If a pencil-beam RF pulse is used for excitation, then respiration signals with high amplitude can be obtained, but loud noise is generated due to quick and steep gradient magnetic field changes
Solution Approach 1:
The invention extracts the beneficial aspect of pencil-beam RF pulses (high amplitude respiration signals) by using non-selective RF pulses for excitation, which also provide low noise. Then, it compensates for the loss in signal amplitude by selectively choosing the coil element that provides the highest respiration signal amplitude through frequency spectrum analysis, thereby achieving both low noise and high signal quality simultaneously.
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
Enables the selection of optimal coil elements for determining respiration signals with high quality, reducing noise and artifacts, and improving the reflection of respiratory motion in MRI images.
Implementation Method 1
a magnetic resonance imaging apparatus for obtaining body-motion signals from a subject
Implementation Method 2
a gradient magnetic field is quickly and steeply changed
Data Source
Figure 1
Figure 2(a)~2(b)
Figure 3
AI summary
An MRI apparatus comprising a signal analyzing unit 91 for determining a feature quantity of a navigator signal, and obtaining data representing a temporal change of the feature quantity for each coil element; a transforming unit 92 for transforming the data obtained for each coil element into frequency spectra FS1 to FS16; and a selecting unit 93 for selecting a coil element for determining a signal value of a body-motion signal for the subject from among coil elements E1 to E16 based on the frequency spectra FS1 to FS16.