Multiscale MRI Motion Encoding Gradients Using Basis Functions
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Solution Overview
Problem
Current magnetic resonance elastography (MRE) techniques face challenges in accurately encoding and reconstructing broadband tissue motion, particularly in multi-frequency and transient scenarios, due to the complexity and time-consuming nature of selecting motion encoding gradients (MEGs) that are traditionally shaped uniformly across timepoints.
Innovation Solution
The use of motion encoding gradients shaped based on wavelet basis functions, such as Haar wavelets or Fourier series, allows for multiscale encoding of tissue motion, enabling improved detection and decoding of broadband tissue motion through inverse transforms like Haar or Fourier transforms, simplifying data processing and enhancing contrast and signal-to-noise ratio.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If motion encoding gradients are shaped uniformly across all timepoints, then the MEG selection process becomes simpler, but the ability to accurately depict broadband tissue motion in multi-frequency and transient scenarios deteriorates
Solution Approach 1:
The patent segments the MEG waveform into multiple timepoints, allowing each timepoint to have a differently shaped MEG tailored to the specific motion characteristics at that time. This segmentation enables accurate depiction of broadband and multi-frequency tissue motion while maintaining systematic control through the framework of basis function expansion.
Solution Approach 2:
The patent introduces temporal variability into the MEG waveforms by allowing the gradient shapes to change across different timepoints. This dynamic approach enables the MEGs to adapt to transient and multi-frequency tissue motion characteristics, improving measurement precision without requiring manual selection of complex waveforms.
2Measurement precision
If motion encoding gradients are carefully selected to depict broadband motion, then the accuracy of tissue motion depiction improves, but the reconstruction process becomes more complex and time-consuming
Solution Approach 1:
The patent changes the parameterization of MEG waveforms by expressing them as expansions in terms of basis functions with time-varying coefficients. This parameter change simplifies the reconstruction process by reducing the number of independent parameters that need to be determined, thereby decreasing computational complexity while maintaining accurate depiction of broadband tissue motion.
Solution Approach 2:
The patent employs a universal basis function framework that can represent various MEG waveform shapes through different coefficient combinations. This universal approach allows a single reconstruction algorithm to handle diverse motion patterns (broadband, multi-frequency, transient) without requiring separate complex processing for each case, thereby reducing overall reconstruction complexity.
3Measurement precision
If motion encoding gradients are carefully selected for broadband motion, then the fidelity of tissue motion representation improves, but the time required for reconstruction increases
Solution Approach 1:
By parameterizing MEG waveforms using basis function expansions, the patent reduces the dimensionality of the reconstruction problem. This parameter change enables faster computation of tissue motion from MEG data while preserving the fidelity of broadband and transient motion representation, thereby reducing reconstruction time.
Solution Approach 2:
The patent performs preliminary decomposition of MEG waveforms into basis function components during the encoding phase. This preliminary action simplifies subsequent reconstruction operations by pre-organizing the gradient information in a form that enables rapid inversion and tissue motion calculation, thus reducing the time required for final reconstruction.
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
This approach facilitates more robust encoding and decoding of multifrequency tissue motions, simplifying image reconstruction and improving mechanical property estimation in MRE, thereby enhancing the accuracy and efficiency of MRE techniques.
Implementation Method 1
Phase contrast magnetic resonance imaging ('PC-MRI') is a group of MRI techniques that detect tissue motion
Implementation Method 2
a gradient system including at least one gradient coil configured to generate a magnetic field gradient
Implementation Method 3
MRE data are acquired while mechanical waves are propagating in at least one tissue of the subject
Data Source
AI summary
Described here are systems and methods for magnetic resonance imaging using motion encoding gradients that are shaped based on one or more basis functions of an integral transform. These motion encoding gradients are capable of encoding broadband tissue motion. The motion encoding gradients can implement multiple scales, shapes, or variations of one or more basis functions, series, or systems to facilitate encoding and detecting broadband tissue motion. The underlying tissue motion can be decoded using an inverse transform corresponding to the inverse of the integral transform on which the one or more basis functions are based.