Multi-Coil MRI Weight Synthesis for Phase-Sensitive Imaging
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Solution Overview
Problem
In multi-point MRI studies, increasing the amount of autocalibration data to improve image reconstruction quality increases acquisition time, necessitating a method to efficiently combine k-space data from multiple time points for accurate image reconstruction.
Innovation Solution
Combining training data sets from multiple time points to calculate a set of weights for synthesizing unacquired k-space data, allowing for improved accuracy in image reconstruction while retaining phase information and reducing acquisition time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the amount of autocalibration data is increased to improve image reconstruction quality, then the accuracy of image reconstruction is improved, but the acquisition time increases
Solution Approach 1:
The patent combines training data sets from multiple time points to calculate a single set of weights for parallel imaging reconstruction. By merging data from multiple time points, the method improves the accuracy of weight calculation and consequently enhances image reconstruction quality without requiring increased autocalibration data at each individual time point, thereby avoiding increased acquisition time.
2Measurement precision
If training data from multiple time points is combined to improve weight calculation accuracy, then the accuracy of image reconstruction is improved, but the complexity of data processing increases
Solution Approach 1:
The patent performs preliminary combination of training data sets from multiple time points before the actual weight calculation process. By pre-processing and combining the training data in advance, the method simplifies the subsequent weight calculation step while maintaining high accuracy, as the combined training data set already contains all necessary information from multiple time points in an organized format.
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 enhances the accuracy of image reconstruction in multi-point studies by improving the calculation of coefficients, reducing acquisition time, and maintaining phase information, making it suitable for phase-sensitive applications like chemical species separation.
Implementation Method 1
Magnetic resonance imaging (MRI) uses a powerful magnet to create a strong, uniform, static magnetic field... the nuclear spins that are associated with the hydrogen nuclei in tissue water become polarized... resonance frequency of the hydrogen nuclei
Implementation Method 2
Radio frequency (RF) coils are used to create pulses of RF energy at or near the resonance frequency of the hydrogen nuclei... As the nuclear spins then relax back to their rest energy state, they give up energy in the form of an RF signal
Implementation Method 3
gradient coils that produce smaller amplitude, spatially varying magnetic fields when current is applied to them... create a small ramp on the magnetic field strength, and concomitantly on the resonance frequency of the nuclear spins, along a single axis
Implementation Method 4
The resultant raw data fills a 2D or 3D k-space matrix which is then 'reconstructed' into images using Fourier transformation techniques... parallel imaging techniques... use spatial sensitivity profiles of the individual receiver coils in addition to traditional gradient spatial encoding techniques to recover the MRI signals
Data Source
AI summary
A method for determining weights (or coefficients) for synthesizing k-space data for autocalibrated parallel imaging (API) combines training data sets (including k-space data such as autocalibrating signals (ACS)) acquired at multiple successive time points. Combining training data sets from multiple successive time points together to determine a set of weights increases the accuracy of the calculated weights. The weights may be applied to k-space data from a single or multiple time points. The method retains the phase information of the individual time point images and may thus be applied, for example, to phase-sensitive multi-point imaging such as chemical species separation studies.