MR Image Reconstruction Using Navigator-Guided Compressed Sensing
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Solution Overview
Problem
Existing MR image reconstruction methods, such as XD-GRASP, suffer from reduced image quality when using a Cartesian sampling scheme, particularly for respiratory phases with large respiratory displacements, and have low scan efficiency due to motion gating requirements.
Innovation Solution
A method that incorporates navigator signals to sort k-space data into bins representing different motion states and uses a compressed sensing procedure with a data consistency and transform sparsity component, where motion information is used to correct for motion within bins, allowing for robust reconstruction even with Cartesian sampling and near 100% scan efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If motion gating is used to minimize respiratory motion artifacts, then image quality is improved, but scan efficiency decreases and scan time increases
Solution Approach 1:
The patent uses navigator echoes to continuously monitor diaphragm position and provides feedback signals that guide the sorting of k-space data into motion-state bins. This feedback mechanism enables real-time motion compensation without requiring motion gating, thereby maintaining both image quality and scan efficiency.
Solution Approach 2:
The patent dynamically sorts k-space data into multiple bins based on real-time respiratory motion states detected by navigator echoes. This dynamic binning approach adapts to changing motion conditions during the scan, allowing efficient use of all acquired data while compensating for motion artifacts.
2Productivity
If Cartesian sampling scheme is used for k-space acquisition, then scan efficiency is improved, but image quality deteriorates due to motion artifacts
Solution Approach 1:
The patent applies dynamic binning to Cartesian-sampled k-space data, sorting it into multiple bins according to respiratory motion states. This dynamic organization compensates for the inherent sensitivity of Cartesian sampling to motion, maintaining image quality while preserving the efficiency benefits of Cartesian acquisition.
Solution Approach 2:
The patent segments the Cartesian k-space data into multiple bins corresponding to different respiratory motion states. By processing each bin separately with appropriate motion compensation, the method overcomes the limitations of Cartesian sampling while maintaining its efficiency advantages.
3Manufacturing precision
If navigator pulses are applied to track diaphragm motion, then motion compensation is improved, but device complexity increases
Solution Approach 1:
The patent uses navigator echoes that serve multiple functions: they track diaphragm motion for motion state detection and also provide feedback for k-space data sorting. This multi-functionality reduces the need for separate motion monitoring systems, thereby limiting the increase in device complexity.
Solution Approach 2:
The navigator echoes are acquired as part of the standard imaging sequence itself, using the same RF pulses and gradient schemes. The imaging sequence serves its own motion monitoring needs, eliminating the requirement for additional dedicated motion tracking hardware or sequences.
4Productivity
If all k-space data is acquired and used, then scan efficiency approaches 100%, but motion artifacts increase without compensation
Solution Approach 1:
The patent dynamically bins all acquired k-space data according to real-time respiratory motion states detected by navigator echoes. This dynamic organization allows 100% utilization of acquired data while applying appropriate motion compensation to each bin, preventing motion artifacts even with complete data acquisition.
Solution Approach 2:
The patent segments all k-space data into multiple bins corresponding to different respiratory phases. By processing each segment with motion-specific compensation, the method achieves both complete data utilization and artifact suppression, maintaining image quality while maximizing scan efficiency.
Data Source
AI summary
In a method for reconstructing magnetic resonance (MR) image data from k-space data, k-space data of an image region of a subject are provided to a computer that is also provided with multiple navigator signals for the image region of the subject. The computer sorts the k-space data into multiple bins, the multiple bins representing different motion states of the subject. For each of the multiple bins, the computer executes a compressed sensing procedure to reconstruct the MR image data from the k-space data in the respective bin. Execution of the compressed sensing procedure includes solving an optimization problem comprising a data consistency component and a transform sparsity component. Motion information is incorporated by the computer into at least one of the data consistency component and the transform sparsity component of the optimization problem.