Pseudo-Random Undersampling MRI Data Acquisition
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Solution Overview
Problem
Current magnetic resonance imaging (MRI) techniques face challenges in achieving sufficient temporal and spatial resolution, particularly in accelerated imaging sequences, which are often inadequate for diagnostic purposes.
Innovation Solution
The implementation of pseudo-random undersampling patterns in magnetic resonance imaging data acquisition, combined with multiplicative constrained reconstruction processes, to generate and reconstruct images from undersampled data sets, allowing for improved temporal and spatial resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If accelerated imaging techniques are used to reduce scan time, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The k-space data acquisition is segmented into multiple time points with different pseudo-random undersampling patterns. Each time point captures a subset of k-space data according to a specific pattern, and the complete data is reconstructed by combining these segmented acquisitions. This segmentation enables accelerated scanning while maintaining image quality through intelligent data distribution.
Solution Approach 2:
The patent employs dynamic pseudo-random undersampling patterns that change across different time points. Instead of using a static sampling pattern, the sampling distribution is dynamically adjusted for each acquisition time point. This dynamic approach allows flexible optimization of temporal and spatial resolution while maintaining high acquisition speed.
2Productivity
If pseudo-random undersampling patterns are used to accelerate data acquisition, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent utilizes parameter changes in the pseudo-random sampling patterns to achieve acceleration. By varying sampling parameters across different time points and using multiplicative constrained reconstruction algorithms, the system achieves 20-fold temporal-spatial acceleration. The parameter changes are designed to be computationally efficient while delivering significant speed improvement.
3Productivity
If less than all information is acquired to speed up imaging, then productivity is improved, but loss of information increases
Solution Approach 1:
The multiplicative constrained reconstruction process incorporates feedback mechanisms that iteratively refine the image reconstruction based on the acquired undersampled data. The algorithm continuously adjusts the reconstruction parameters to maximize image quality while using minimal data, thereby reducing information loss despite the accelerated acquisition.
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 enables accelerated data acquisition and processing, reducing incoherent spatial artifacts and achieving a temporal-spatial acceleration of over 20-fold, while effectively reconstructing high-quality images from non-uniformly sampled data.
Implementation Method 1
The magnetic fields used to generate images in MRI systems include a highly uniform, static magnetic field that is produced by a primary magnet
Implementation Method 2
An RF coil is employed to produce an RF magnetic field. This RF magnetic field perturbs the spins of some of the gyromagnetic nuclei from their equilibrium directions, causing the spins to precess around the axis of their equilibrium magnetization
Implementation Method 3
During this precession, RF fields are emitted by the spinning, precessing nuclei and are detected by either the same transmitting RF coil, or by a separate coil
Data Source
AI summary
The present disclosure is intended to describe embodiments for improving image data acquisition and processing in accelerated dynamic magnetic resonance imaging sequences. One embodiment is described where a method includes an acquisition sequence configured to acquire an undersampled set of magnetic resonance data. The undersampled set of magnetic resonance data has a pseudo-random sampling pattern within a data space acquired at a first time, the pseudo-random sampling pattern being influenced by other pseudo-random sampling patterns within the data space arising from the acquisition of additional undersampled sets of magnetic resonance data at respective times. In some embodiments, the pseudo-random sampling patterns of the undersampled sets of magnetic resonance data interleave to yield a desired sampling pattern.