Joint k-Space Trajectory Estimation and Image Reconstruction
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Solution Overview
Problem
Current fast imaging MRI techniques are limited by the time inefficiencies associated with acquiring navigator data and the limited utility of fixed trajectory maps, which are often required to correct for k-space trajectory errors.
Innovation Solution
A method for jointly estimating the actual k-space trajectory and reconstructing images using a compact joint optimization model that optimizes an objective function to account for deviations between the actual and designed k-space trajectories, reducing the need for costly pre-scans and navigators, and utilizing reduced models for efficient computation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If navigator data acquisition or pre-scan measurements are performed to correct k-space trajectory errors, then trajectory accuracy is improved, but scan time increases
Solution Approach 1:
The patent combines trajectory estimation and image reconstruction into a single joint optimization process. Instead of separately acquiring navigator data and then reconstructing images, the method simultaneously estimates the actual k-space trajectory and reconstructs the image from the acquired data using a unified objective function that models trajectory deviations with adjustment parameters.
Solution Approach 2:
The reconstruction algorithm itself performs trajectory correction by jointly estimating trajectory errors and image content. The system uses the acquired data to automatically determine both the actual trajectory deviated from the designed trajectory and the corresponding image, eliminating the need for separate navigator acquisitions or pre-scan measurements.
2Productivity
If fixed trajectory maps are used to correct k-space trajectory errors, then correction efficiency is improved, but adaptability to different protocols decreases
Solution Approach 1:
The patent replaces fixed, pre-determined trajectory maps with dynamic trajectory estimation that adapts to each specific acquisition. The joint optimization method estimates adjustment parameters that describe actual trajectory deviations specific to each protocol and dataset, allowing the system to automatically adapt to different imaging protocols without requiring separate pre-scan measurements for each protocol.
Solution Approach 2:
The method models trajectory deviations using adjustable parameters that are optimized based on the acquired data. By representing the actual trajectory as the designed trajectory plus deviation terms with adjustable parameters, the system can flexibly adapt to different protocols by optimizing these parameters for each specific case rather than relying on fixed maps.
3Measurement precision
If full pre-scan measurements are performed to map the k-space trajectory, then trajectory characterization accuracy is improved, but computational burden increases
Solution Approach 1:
The patent uses a partial modeling approach where the actual trajectory is represented as the designed trajectory plus deviation terms with a limited number of adjustment parameters. This partial model captures the essential trajectory errors without requiring full pre-scan measurements, reducing computational complexity while maintaining adequate accuracy for correction purposes.
Data Source
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AI summary
Systems and methods for estimating the actual k-space trajectory implemented when acquiring data with a magnetic resonance imaging ("MRI") system while jointly reconstructing an image from that acquired data are described. An objective function that accounts for deviations between the actual k-space trajectory and a designed k-space trajectory while also accounting for the target image is optimized. To reduce the computational burden of the optimization, a reduced model for the parameters associated with the k-space trajectory deviation and the target image can be implemented.