Adaptive MRI Imaging Parameters Using Non-Rectilinear Trajectories
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Solution Overview
Problem
Existing MRI systems lack the ability to automatically adapt imaging parameters in real-time based on non-catheter feedback, such as metabolic, spectroscopic, and anatomic sources, which limits their effectiveness in improving temporal resolution, reducing motion artifacts, and optimizing image quality during procedures like perfusion, diffusion, and BOLD MRI.
Innovation Solution
Implementing non-rectilinear trajectories and non-Fourier pulse sequences that allow for real-time adaptation of imaging parameters based on various feedback sources, including contrast agent uptake, diffusion characteristics, and blood oxygen levels, enabling dynamic adjustment of field of view, spatial resolution, and other parameters to enhance imaging efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional rectilinear trajectories and Fourier pulse sequences are used, then the MRI system operates with standard imaging parameters, but temporal resolution is limited and motion artifacts increase
Solution Approach 1:
The patent implements dynamic adaptation of imaging parameters by transitioning from static, pre-set parameters to real-time adjustable parameters based on feedback from non-catheter sources. The system continuously monitors physiological signals and adjusts trajectory, resolution, and other parameters dynamically during the MRI procedure to optimize temporal resolution and reduce motion artifacts.
Solution Approach 2:
The system incorporates feedback loops that receive real-time physiological data from non-catheter sources (such as ECG, respiratory signals, or functional MRI data) and use this feedback to automatically adjust imaging parameters. This closed-loop control enables the system to respond to physiological changes and optimize image quality without manual intervention.
2Manufacturing precision
If imaging parameters are manually adjusted, then some optimization is achieved, but the process is time-consuming and reduces productivity
Solution Approach 1:
The MRI system performs self-adjustment of imaging parameters by automatically processing feedback signals and modifying acquisition parameters without requiring operator intervention. The system autonomously optimizes trajectory, resolution, and other parameters based on real-time physiological conditions, eliminating the need for manual parameter tuning while maintaining high image quality.
Solution Approach 2:
The system implements automatic changes to multiple imaging parameters including trajectory type, spatial resolution, field of view, and pulse sequence timing based on real-time feedback. These parameter changes are executed automatically to optimize image quality and reduce motion artifacts without compromising imaging efficiency or requiring manual intervention.
3Measurement precision
If higher magnetic fields are used, then signal-to-noise ratio improves, but patient exposure to higher energy fields increases
Solution Approach 1:
The system uses non-rectilinear trajectories that oversample the center of k-space, providing redundant information that improves signal-to-noise ratio without requiring proportionally higher magnetic field strengths. This partial oversampling approach achieves enhanced image quality while limiting the need for excessive magnetic field exposure that would increase patient energy exposure.
Solution Approach 2:
The patent combines multiple imaging techniques and feedback sources to achieve optimal signal-to-noise ratio without relying solely on higher magnetic fields. By integrating non-rectilinear trajectories, adaptive parameter adjustment, and multi-source feedback, the system creates a composite imaging approach that maintains high image quality while reducing the need for high-energy field exposure.
4Reliability
If real-time parameter adaptation is implemented, then image quality and temporal resolution improve, but system complexity increases
Solution Approach 1:
The system employs a unified feedback processing framework that handles multiple types of physiological signals (ECG, respiratory, functional MRI data) through a common adaptation mechanism. This multi-functional approach allows the same core system to optimize various imaging parameters across different physiological conditions and imaging protocols, reducing overall system complexity despite the enhanced adaptability.
Data Source
AI summary
Systems, methodologies, media, and other embodiments associated with automatically adapting MRI controlling parameters are described. One exemplary method embodiment includes configuring an MRI apparatus to acquire MR signal data using a non-rectilinear trajectory. The example method may also include acquiring MR signals, transforming the MR signals into image data, and selectively adapting the MRI controlling parameters based, at least in part, on information associated with the MR signals.