MRI Radial K-Space Spoke Distribution for Motion Artifact Reduction
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Solution Overview
Problem
Current MRI techniques are inadequate in correcting for patient motion, leading to inferior image quality due to corrupted signals in localized regions of the MRI k-Space, especially with methods like Radial Stack-of-Stars imaging and golden-angle or interleaved view-ordering techniques.
Innovation Solution
A method that estimates patient motion in real-time during MRI data acquisition, adjusts the view order sequence to distribute radial k-space spokes with similar motion evenly across the k-Space, and reconstructs the image based on this adjusted distribution to improve image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Radial Stack-of-Stars imaging with golden-angle or interleaved view-ordering is used, then patient motion correction is attempted, but corrupted signal occurs in localized regions of k-Space resulting in inferior image quality
Solution Approach 1:
The view ordering sequence is made dynamic and adaptive rather than fixed. The system continuously monitors patient motion and adjusts the view ordering in real-time to accommodate detected motion, transforming a static acquisition protocol into a dynamic one that responds to changing conditions.
Solution Approach 2:
Different regions of k-Space are treated differently based on their sensitivity to motion. The system identifies localized corrupted regions and applies targeted correction strategies, allowing high-quality data from unaffected regions to compensate for corrupted areas rather than treating the entire k-Space uniformly.
2Device complexity
If fixed view order sequence is used for data acquisition, then acquisition process is simple, but patient motion causes corrupted signal in localized regions
Solution Approach 1:
The system implements a feedback loop where patient motion is continuously monitored and detected, and this information feeds back to adjust the view ordering sequence. The processor modifies subsequent data acquisition based on detected motion patterns, creating a closed-loop control system that adapts to patient movement.
Solution Approach 2:
The system performs preliminary motion detection and characterization before completing the full data acquisition. By identifying motion patterns early in the process, the system can pre-adjust the view ordering sequence to prevent corruption before it occurs, rather than reacting after damage is done.
3Reliability
If motion detection and view order adjustment is implemented, then image quality improves, but acquisition process becomes more complex
Solution Approach 1:
The system performs self-correction by automatically detecting motion artifacts and adjusting its own acquisition parameters without external intervention. The processor autonomously modifies the view ordering sequence based on detected motion, making the system self-regulating and reducing the need for manual adjustment or complex external control mechanisms.
Solution Approach 2:
The system changes acquisition parameters dynamically, specifically the view ordering sequence, in response to detected motion. By modifying these parameters in real-time, the system adapts to motion conditions and maintains image quality without requiring fundamentally different hardware or overly complex processing architecture.
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 enhances image quality by reducing motion artifacts and ensuring more even distribution of k-space spokes, thereby minimizing gaps and corrupted spatial frequencies, resulting in improved MRI image reconstruction.
Implementation Method 1
MRI uses a magnet having a strong magnetic field to generate a static magnetic field B0. When a part of the human body to be imaged is positioned in the static magnetic field B0, nuclear spin associated with hydrogen nuclei in human tissue is polarized
Implementation Method 2
nuclear spin associated with hydrogen nuclei in human tissue is polarized, so that the tissue of the to-be-imaged part generates a longitudinal magnetization vector at a macroscopic level
Implementation Method 3
After a radio-frequency field B1 intersecting the direction of the static magnetic field B0 is applied, the direction of rotation of protons changes so that the tissue of the to-be-imaged part generates a transverse magnetization vector at a macroscopic level
Implementation Method 4
After the radio-frequency field B1 is removed, the transverse magnetization vector decays in a spiral manner until it is restored to zero. A free induction decay signal is generated during decay
Data Source
AI summary
A method for generating an image of an object with a magnetic resonance imaging (MRI) system includes acquiring an initial set of radial k-space spokes. Based on the initial set of radial k-space spokes, a plurality of motion states of the object is estimated. A first set of radial k-space spokes are then acquired based on a pre-defined view order sequence. Based on the first set of radial k-space spokes, a motion of the object is determined. If the object motion is detected, then the pre-defined view order is adjusted. A second set of radial k-space spokes is then acquired based on the adjusted pre-defined view order. The MRI k-space is generated by distributing the radial k-space spokes having similar motion evenly across the MRI k-space. Finally, the image of the object is reconstructed based on the MRI k-space.