K-space Data Correction for MRI Signal Variation Compensation
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Solution Overview
Problem
Magnetic resonance (MR) imaging applications face challenges due to dynamic MR signals causing inconsistent k-space data, leading to imaging artifacts and a trade-off between image quality and acquisition speed, as signal variation over time results in blurring or streaking artifacts.
Innovation Solution
A k-space data correction method involving a calibration scan to generate a signal variation model, which is applied to subsequent k-space data to compensate for signal changes, thereby reducing artifacts and improving image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a small acquisition window is used to reduce signal variation, then image quality is improved, but acquisition speed deteriorates due to repeated scans
Solution Approach 1:
The patent performs a preliminary calibration scan to measure the actual signal variation characteristics of the imaged object. This preliminary measurement enables the system to predict and compensate for signal decay during the actual imaging scan, allowing the use of longer acquisition windows without sacrificing image quality.
Solution Approach 2:
The patent dynamically adjusts the compensation parameters based on the measured signal variation model. By changing the compensation factors applied to different k-space data points, the system corrects for T2 decay effects and maintains image quality while enabling faster acquisition with longer windows.
2Productivity
If a longer acquisition window is used to increase acquisition speed, then productivity is improved, but image quality deteriorates due to signal variation and blurring artifacts
Solution Approach 1:
The patent uses feedback from the calibration scan to continuously refine the signal variation model. This feedback mechanism allows the system to adapt the compensation strategy to the specific characteristics of each imaged object, maintaining image quality even with longer acquisition windows that capture more signal decay.
Solution Approach 2:
By performing the calibration scan beforehand, the system prepares the compensation model in advance, enabling the main imaging scan to proceed faster without compromising quality. The preliminary characterization of signal decay allows real-time correction during the accelerated acquisition.
3Measurement precision
If multiple repetitions are performed to maintain signal consistency, then measurement precision is improved, but loss of time increases due to extended scan duration
Solution Approach 1:
The patent changes the temporal parameter by using a single longer acquisition window instead of multiple shorter repetitions. The signal variation model compensates for the extended time, allowing the system to maintain measurement precision while reducing the total scan duration by eliminating the need for repeated acquisitions.
4Productivity
If TSE sequence with longer acquisition window is used to improve productivity, then productivity is improved, but object-generated harmful factors increase due to blurring and streaking artifacts
Solution Approach 1:
The patent converts the harmful effect of signal decay (which causes blurring and streaking artifacts) into a measurable characteristic through the calibration scan. By characterizing the decay pattern, the system applies compensation that transforms what would be harmful signal variation into useful information for artifact correction, enabling TSE sequences to run at higher speeds without producing degradation artifacts.
Data Source
AI summary
A system for performing magnetic resonance imaging (MRI) of a subject has a pulse sequence system that generates a pulse sequence and has a gradient system, a plurality of gradient coils, a radio-frequency system, and a plurality of RF coils. The pulse sequence system causes the subject to emit MR signals which are captured as k-space data. The system also has a k-space ordering processor that collects first k-space data and second k-space data, an MR signal modeler that generates a signal variation model, and a compensation module that applies the signal variation model to the second k-space data collected to produce compensated k-space data. A display processor reconstructs the compensated k-space data into an image of the subject. The compensated data accounts for variation in magnetization during the pulse sequence and k-space data collection to reduce artifacts in the images.