K-space Sampling Density for MRI Time Resolution
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Solution Overview
Problem
Current magnetic resonance imaging techniques face challenges in achieving high time resolution, particularly in dynamic studies like magnetic resonance angiography, where rapid series of images are needed to capture the contrast agent's progression through the vascular system, often requiring longer data acquisition times.
Innovation Solution
The technique involves dividing k-space into a central and peripheral region, sampling the central region at a higher density and the peripheral region at a lower density, allowing for faster image updates by prioritizing the acquisition of low k-space lines, and using a non-linear scanning trajectory such as a spiral path, compatible with partial parallel acquisition methods like GRAPPA.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If uniform sampling density is applied to all k-space regions, then manufacturing precision is maintained, but productivity decreases due to longer acquisition times
Solution Approach 1:
The patent applies different sampling densities to different regions of k-space: the central region is sampled at a higher density while the peripheral region is sampled at a lower density. This local differentiation allows the central region (which contains the most important image information) to be captured with high quality, while reducing the overall acquisition time by sampling the peripheral region less frequently.
2Loss of time
If central k-space region is sampled more frequently, then time resolution improves, but manufacturing precision deteriorates in peripheral regions
Solution Approach 1:
The patent implements periodic sampling of the central k-space region at a higher frequency than the peripheral region. By periodically updating the central region data more frequently, the method achieves improved time resolution for capturing dynamic contrast agent flow, while the peripheral region is sampled less frequently to reduce overall acquisition time.
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 significantly reduces the time between successive image acquisitions, enhancing time resolution and allowing for better detection and assessment of vascular diseases with reduced contrast agent usage, while being compatible with existing methods like GRAPPA and ECG triggering.
Implementation Method 1
Magnetic resonance imaging is a widely used image modality, wherein an examination subject is moved into a strong, static basic magnetic field to cause nuclear spins in the examination subject, that were previously randomly oriented, to become aligned with the direction of the basic magnetic field. Radio-frequency (RF) energy is then radiated into the examination subject, causing the nuclear spins to be deflected from their aligned orientation. As the nuclear spins precess upon returning to the aligned orientation, they emit RF magnetic resonance signals
Implementation Method 2
For the purpose of spatially encoding the magnetic resonance signals, the examination subject is also in the presence of gradient fields, respectively generated by gradient coils, the gradients field typically being oriented along the respective axes of a Cartesian coordinate system
Data Source
AI summary
In a method for operating a magnetic resonance imaging system to generate a magnetic resonance data file, raw magnetic resonance data are acquired, and k-space is established in a computerized storage medium, with k-space being divided into a contiguous central region and a contiguous region surrounding the central region. In a computerized procedure, the raw data are entered into k-space at a constant sampling rate for both of the central and peripheral regions, while sampling the central region with a first density of sampling points, and sampling the peripheral region at a second density of sampling points that is less than the first sampling density. The set of data points thereby representing sampled k-space is made available in a data file as an output from the computerized procedure, in a form allowing an image to be reconstructed from the contents of the data file.