Maxwell Parallel Imaging Reconstruction Algorithm
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Solution Overview
Problem
Current magnetic resonance imaging (MRI) techniques are time-consuming, costly, and require expensive magnets, leading to long MR scan times and a confining environment for patients, which degrades the user experience and increases costs.
Innovation Solution
A computer system that determines coefficients for representing coil sensitivities and MR information associated with a sample, using a nonlinear optimization problem and a forward model to simulate MR signals, allowing for reduced MR scan times by skipping and reconstructing MR scan lines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional MRI techniques are used to achieve high-spatial resolution, then image quality is improved, but scan time increases and cost increases
Solution Approach 1:
The system performs preliminary action by pre-calculating and storing coil sensitivity maps and basis vectors before the actual MRI scan. This preprocessing allows the reconstruction algorithm to work more efficiently during scanning, reducing the overall scan time while maintaining image quality through optimized parallel imaging reconstruction
Solution Approach 2:
The invention creates a computational model (copy) of the MRI physics process through forward models that simulate signal generation. This virtual model allows the system to predict and reconstruct images from undersampled data, effectively creating multiple image copies from fewer measurements, thereby reducing scan time without sacrificing image quality
2Measurement precision
If traditional MRI techniques are used to achieve high-spatial resolution, then image quality is improved, but cost increases
Solution Approach 1:
The system changes key parameters of the MRI acquisition process, specifically the sampling pattern and undersampling factor, to optimize the balance between image quality and cost. By adjusting these parameters and using advanced reconstruction algorithms, the system achieves high-resolution imaging with fewer measurements, reducing the need for expensive, high-field magnets and long scan times
3Productivity
If scan lines are skipped to reduce scan time, then productivity is improved, but measurement precision may deteriorate
Solution Approach 1:
The reconstruction algorithm incorporates feedback mechanisms that iteratively refine the image estimate by comparing the reconstructed image with the acquired undersampled data. This feedback loop allows the system to recover missing information from skipped scan lines by exploiting the redundancy and correlations in the data, thereby maintaining image quality despite reduced sampling
Solution Approach 2:
The invention introduces intermediary elements including coil sensitivity maps and basis vectors that act as mediators between the undersampled data and the final image reconstruction. These intermediaries contain pre-computed information about the coil geometries and signal characteristics, enabling accurate reconstruction from fewer measurements and allowing scan lines to be skipped without significant loss of image quality
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 system significantly reduces MR scan times, improves user experience, and decreases costs by accelerating the MRI process while maintaining image quality.
Implementation Method 1
a forward model that uses the MR information as inputs and simulates response physics of the sample to output computed MR signals
Implementation Method 2
solve, on a voxel-by-voxel basis for voxels associated with the sample, a nonlinear optimization problem for the MR information associated with the sample and the coefficients
Data Source
AI summary
During operation, a computer system may acquire magnetic resonance (MR) signals associated with a sample from a measurement device or memory. Then, the computer system may access a predetermined set of coil magnetic field basis vectors associated with a surface surrounding the sample, where coil sensitivities of coils in the measurement device are represented by weighted superpositions of the predetermined set of coil magnetic field basis vectors using coefficients, and where the predetermined coil magnetic field basis vectors are solutions to Maxwell's equations. Next, the computer system may solve, on a voxel-by-voxel basis for voxels associated with the sample, a nonlinear optimization problem for MR information associated with the sample and the coefficients using: a forward model that uses the MR information as inputs and simulates response physics of the sample, the MR signals and the predetermined set of coil magnetic field basis vectors.