Diffusion-Weighted MRI k-Space Matrix Segmentation
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Solution Overview
Problem
Current magnetic resonance imaging techniques face limitations in accurately generating diffusion-weighted image data, particularly at high diffusion gradients, due to restrictions in gradient coil design and the need for longer acquisition times, which can result in noisy data and reduced image quality.
Innovation Solution
The method involves acquiring raw data with different diffusion weightings and assigning them to distinct k-space matrices, allowing for the determination of diffusion-weighted image data that compensates for differences between these matrices, enabling the use of higher diffusion gradients without extrapolation and maintaining consistent echo times and RF pulse timing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If higher diffusion gradients are used to improve diffusion-weighted image data accuracy, then measurement precision is improved, but acquisition time increases and noise increases
Solution Approach 1:
The patent divides the k-space matrix into multiple segments or regions, allowing different diffusion weightings to be applied to different segments. This enables the system to acquire data with high diffusion gradients for accuracy while maintaining manageable acquisition times by processing only portions of k-space at any given time, thus resolving the contradiction between measurement precision and time loss.
Solution Approach 2:
The patent dynamically adjusts diffusion weighting parameters and k-space matrix configurations during the acquisition process. By making the diffusion weighting factor variable rather than fixed, the system can optimize the balance between gradient strength for accuracy and acquisition time requirements, allowing adaptive adjustment that prevents excessive time loss while maintaining precision.
2Measurement precision
If higher diffusion gradients are used to improve diffusion-weighted image data accuracy, then measurement precision is improved, but image quality deteriorates due to noise
Solution Approach 1:
The patent applies different diffusion weightings and processing characteristics to different regions of the k-space matrix. By localizing high diffusion gradient applications to specific regions rather than uniformly across all k-space, the system improves measurement precision where needed while minimizing noise introduction in other regions, thus resolving the contradiction between precision and image quality.
Solution Approach 2:
The patent introduces an intermediary processing step that involves adjusting k-space matrix parameters between acquisition and final image reconstruction. This intermediary adjustment allows the system to compensate for noise introduced by high diffusion gradients through mathematical processing, thereby maintaining image quality while preserving measurement precision.
3Measurement precision
If diffusion gradients are increased to avoid extrapolation, then measurement precision is improved, but device complexity increases due to gradient coil restrictions
Solution Approach 1:
The patent changes the parameter configuration of the k-space matrix and diffusion weighting factors to achieve high measurement precision without requiring proportionally higher gradient strengths. By adjusting parameters such as echo time, diffusion weighting factor, and k-space sampling patterns, the system achieves accurate diffusion-weighted image data while avoiding the need for more complex gradient coil designs.
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 allows for the generation of accurate diffusion-weighted image data, particularly at high b-values, by adjusting the k-space matrices to accommodate varying diffusion weightings, reducing noise, and maintaining image quality, while avoiding limitations imposed by gradient coil restrictions.
Implementation Method 1
a magnetic field gradient is applied by a gradient coil arrangement
Implementation Method 2
Due to the resulting precession of the nuclear spins, radio-frequency signals, known as magnetic resonance signals (MR signals), are emitted
Implementation Method 3
Radio-frequency, excitation signals (RF signals) are then transmitted by suitable antennas in order to tip the nuclear spins of specific atoms excited into resonance by the radio-frequency field
Implementation Method 4
Associated image data can be reconstructed from the raw data by a multidimensional Fourier transformation, for example
Data Source
AI summary
In a method and magnetic resonance (MR) apparatus for determining diffusion-weighted image data, first raw data are acquired with a first diffusion weighting, and the first raw data are assigned to a first k-space matrix. Second raw data are acquired with a second diffusion weighting, and the second raw data are assigned to a second k-space matrix. The first k-space matrix and the second k-space matrix are different from one another at at least one position. The diffusion-weighted image data are determined in a processor based on the first raw data and the second raw data.