MRI Reconstruction Using Mixed K-Space Trajectories
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Solution Overview
Problem
Current MRI techniques using ultra-short echo time pulse sequences, such as PETRA, require a long time to acquire high-resolution images due to the need for extensive sampling along non-Cartesian radial trajectories, which increases scanning duration and is inefficient.
Innovation Solution
The proposed method combines compressed sensing and parallel imaging to reconstruct MRI images from mixed-trajectory data, where the central region of k-space is sampled fully along a Cartesian trajectory and the peripheral region is undersampled along a non-Cartesian trajectory, using sparse representations and sensitivity distribution information to suppress artifacts and accelerate image reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full sampling along non-Cartesian radial trajectories is performed to satisfy the Nyquist criterion, then image quality suitable for medical diagnosis is achieved, but scanning duration increases significantly
Solution Approach 1:
The patent segments k-space into two distinct regions: a central region sampled along Cartesian trajectories and a peripheral region sampled along non-Cartesian radial trajectories. This segmentation allows different sampling strategies to be applied to different parts of k-space, optimizing both image quality and scanning efficiency. The central region contains low-frequency information essential for overall image structure, while the peripheral region contains high-frequency information for fine details.
Solution Approach 2:
The patent applies different sampling densities and trajectories to different regions of k-space. The central region is fully sampled along Cartesian trajectories to ensure accurate low-frequency information, while the peripheral region uses undersampled non-Cartesian trajectories. This local differentiation in sampling quality allows the system to maintain diagnostic image quality while reducing overall scanning time.
2Manufacturing precision
If extensive sampling along non-Cartesian radial trajectories is performed, then high-resolution images are obtained, but reconstruction complexity increases
Solution Approach 1:
The reconstruction process is segmented into two independent parts: one for Cartesian-sampled central k-space data and another for non-Cartesian-sampled peripheral k-space data. This segmentation simplifies the overall reconstruction complexity by allowing each region to be processed with appropriate algorithms rather than requiring a single complex reconstruction method for the entire k-space.
Solution Approach 2:
The patent merges the reconstruction results from the central Cartesian region and the peripheral non-Cartesian region to produce the final image. By combining separately processed regions, the system leverages the simplicity of Cartesian reconstruction for the central region while applying compressed sensing only where necessary in the peripheral region, thereby reducing overall computational complexity.
3Loss of time
If undersampling is applied to reduce scanning duration, then image quality deteriorates due to artifacts and noise
Solution Approach 1:
The patent applies undersampling partially, only to the peripheral region of k-space, while maintaining full sampling in the central region. This partial undersampling approach reduces scanning duration without excessively compromising image quality, as the critical low-frequency information in the central region remains fully sampled. The compressed sensing technique then recovers the missing high-frequency information from the undersampled peripheral region.
Solution Approach 2:
The patent changes the sampling parameters differently for different k-space regions. The central region uses Cartesian sampling with full density, while the peripheral region uses non-Cartesian sampling with reduced density. This parameter differentiation allows the system to optimize the trade-off between scanning duration and image quality by applying undersampling only where it has minimal impact on diagnostic accuracy.
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 reduces the duration of MRI signal acquisition while maintaining image quality suitable for medical diagnosis, allowing for faster and more efficient image reconstruction with improved resolution, while retaining the benefits of ultra-low noise and patient comfort.
Implementation Method 1
magnetic resonance imaging (MRI) is a medical imaging technology in which an antenna is used to irradiate an object with RF pulse signals under certain magnetic field conditions
Implementation Method 2
an antenna is used to irradiate an object with RF pulse signals under certain magnetic field conditions
Implementation Method 3
RF pulses with the Larmor frequency cause spin nucleons in the irradiated object, such as hydrogen nuclei (i.e. H+), to precess at an angle of deflection
Implementation Method 4
under gradient magnetic field control
Data Source
AI summary
The present disclosure is directed to MRI techniques. The techniques include occupying a central region of a first k-space with full sampling along a Cartesian trajectory, occupying a peripheral region of the first k-space with undersampling along a non-Cartesian trajectory; acquiring sensitivity distribution information of receiving coils; based on a sensitivity distribution chart, merging the Cartesian data of the central region according to multiple channels to obtain a third k-space; based on the sensitivity distribution chart, applying parallel imaging and compressed sensing to the undersampled non-Cartesian trajectory to reconstruct an image, obtaining a second k-space by transformation, and when the second k-space and third k-space are synthesized, using a central region of the second k-space to replace the third k-space of a corresponding region to obtain a k-space suitable for image reconstruction.