MR Tensor Field Mapping Without Fourier Transform Reconstruction
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Solution Overview
Problem
Existing non-invasive characterization techniques, such as MRI, are time-consuming, costly, and confining, with long scan times and the need for large homogeneous magnetic fields, degrading user experience and reducing throughput.
Innovation Solution
A system that iteratively modifies parameters using a forward model and RF pulse sequences to determine physical parameters without Fourier transforms, reducing scan time and improving accuracy by concurrently measuring and calculating magnetization components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional MRI techniques are used to determine physical parameters, then measurement precision is improved, but loss of time increases due to long scan times
Solution Approach 1:
The system performs preliminary actions by concurrently measuring multiple magnetization components and calculating predicted components during the scanning process. This allows the system to prepare and process multiple parameters simultaneously rather than sequentially, reducing overall scan time while maintaining characterization accuracy through iterative convergence of measurements with the forward model
Solution Approach 2:
The system implements continuous useful action by concurrently performing measurements and calculations throughout the scanning process. Multiple magnetization components are measured and predicted values are calculated continuously rather than in separate discrete steps, maximizing the utilization of scanning time and improving throughput without sacrificing measurement precision
2Measurement precision
If large homogeneous magnetic fields are used in MRI, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system changes parameters by using multiple different magnetization components (Mx, My, Mz) and their predicted counterparts (Mx_pred, My_pred, Mz_pred) in the iterative convergence process. By varying the parameters being measured and calculated concurrently, the system achieves accurate tissue characterization without requiring excessively large homogeneous magnetic fields, thereby reducing device complexity while maintaining measurement precision
Solution Approach 2:
The system implements multi-functionality by using a single MRI system to concurrently perform multiple measurements of different magnetization components and calculate multiple predicted components. This universal approach allows the system to achieve accurate physical parameter determination through multiple simultaneous measurement pathways rather than requiring specialized equipment for each measurement type
3Measurement precision
If iterative convergence measurements are performed to determine physical parameters, then measurement precision is improved, but productivity decreases due to reduced throughput
Solution Approach 1:
The system performs preliminary calculations of predicted magnetization components using the forward model during the scanning process itself. By preparing these predicted values concurrently with measurements rather than performing full iterative convergence after scanning, the system reduces post-processing time and improves throughput while maintaining the precision benefits of iterative convergence
Solution Approach 2:
The system maintains continuous useful action by performing both measurements and predictive calculations throughout the scanning process. This concurrent execution ensures that the iterative convergence process is efficiently utilized during data acquisition rather than as a separate time-consuming post-processing step, thereby improving productivity and throughput while preserving measurement precision
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 scan time, improves user experience, and enhances characterization accuracy by increasing throughput and reducing errors, while allowing for faster and more accurate MR scans.
Implementation Method 1
magnetic resonance or MR (which is often referred to as 'nuclear magnetic resonance' or NMR), a physical phenomenon in which nuclei in a magnetic field absorb and re-emit electromagnetic radiation
Implementation Method 2
These nuclear spins may precess or rotate around the direction of the external magnetic field at an angular frequency (which is sometimes referred to as the 'Larmor frequency') given by the product of a gyromagnetic ratio of a type of nuclei and the magnitude or strength of the external magnetic field
Data Source
AI summary
During operation, a system may apply an external magnetic field and an RF pulse sequence to a sample. Then, the system may measure at least a component of a magnetization associated with the sample, such as MR signals of one or more types of nuclei in the sample. Moreover, the system may calculate at least a predicted component of the magnetization for voxels associated with the sample based on the measured component of the magnetization, a forward model, the external magnetic field and the RF pulse sequence. Next, the system may solve an inverse problem by iteratively modifying the parameters associated with the voxels in the forward model until a difference between the predicted component of the magnetization and the measured component of the magnetization is less than a predefined value. Note that the calculations may be performed concurrently with the measurements and may not involve performing a Fourier transform.