Magnetic Resonance Image Reconstruction k-Space Displacement Correction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Magnetic resonance imaging systems face inaccuracies and artifacts due to switching dilatation in the gradient field, leading to distorted images and increased costs for manual correction methods.
Innovation Solution
A method that detects raw magnetic resonance data in k-space, determines a displacement value for each trajectory, and shifts readout points to correct for switching dilatation, allowing for automatic and efficient correction of k-space coordinates before image reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If switching sequence of gradient field experiences time deviation (switching dilatation), then spatial resolution accuracy deteriorates, but manual correction methods increase cost and time
Solution Approach 1:
The system automatically determines displacement values from the raw magnetic resonance data itself without requiring external manual intervention. The displacement value determination unit processes the data to calculate correction values, and the readout point displacement unit applies these corrections automatically, making the system self-correcting and eliminating manual correction time and costs.
Solution Approach 2:
The displacement values are determined before the actual image reconstruction process. By calculating the correction values in advance from the raw data and applying them to the readout points prior to reconstruction, the system prevents spatial resolution errors from propagating through the imaging process, thereby improving final image quality without requiring post-processing corrections.
2Reliability
If switching dilatation occurs in gradient field, then image quality deteriorates with artifacts, but automatic correction improves image quality
Solution Approach 1:
The correction process is divided into distinct functional modules: a displacement value determination unit that calculates correction values from raw data, and a readout point displacement unit that applies corrections. This segmentation allows each module to focus on a specific aspect of correction, making the overall complex correction process manageable and systematic.
Solution Approach 2:
The system uses the raw magnetic resonance data itself as feedback to determine the displacement values. By analyzing the actual measured data to calculate correction values and then applying these corrections to the readout points, the system creates a feedback loop that automatically adjusts for switching dilatation effects, improving image reliability without requiring complex external correction mechanisms.
3Measurement precision
If k-space coordinates are not corrected for switching dilatation, then spatial frequency information becomes inaccurate, but correction ensures accurate transformation to positional space
Solution Approach 1:
The displacement values are determined before the final image reconstruction process. By calculating correction values in advance from the raw data and applying them to the readout points prior to reconstruction, the system prepares the k-space data for accurate transformation to positional space without requiring time-consuming post-processing corrections, thereby maintaining high productivity.
Solution Approach 2:
The system automatically determines displacement values from the raw magnetic resonance data itself without requiring external manual intervention. The displacement value determination unit processes the data to calculate correction values, and the readout point displacement unit applies these corrections automatically, making the system self-correcting and eliminating manual correction time and costs.
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 improves image quality and homogeneity by automatically correcting for switching delays, reducing artifacts and costs associated with manual corrections, while enabling faster data acquisition.
Implementation Method 1
The gradient field modifies the resonance frequency (Larmor frequency) and, for example, also the phase position of the magnetization deflected by an RF pulse in a spatially dependent manner
Implementation Method 2
The magnitude of the magnetization (in particular of the transverse magnetization in a plane defined transverse to the previously described basic magnetic field) at a defined location of the examination subject can be determined from the readout point with a Fourier transformation that calculates the signal strength of the signal in the spatial domain from a signal strength (magnitude of the magnetization) that is associated with a specific frequency
Data Source
AI summary
In a method, magnetic resonance system and a reconstruction device for generation of magnetic resonance image data of an examination subject, raw magnetic resonance data are acquired in k-space, the raw magnetic resonance data including measurement values at multiple readout points that are arranged along multiple different trajectories in k-space. A displacement value is determined for each trajectory on the basis of the measurement values of a collection of multiple trajectories. Readout points of the raw magnetic resonance data are displaced by the displacement value, and image data are reconstructed from the displaced raw magnetic resonance data.