Self-Navigated MRI Motion Correction via K-Space Partitioning
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Solution Overview
Problem
Current magnetic resonance (MR) imaging techniques face challenges in accurately capturing motion-induced artifacts, particularly in lung cancer diagnosis, due to respiratory and cardiac motion, which affects image quality and treatment planning, and existing methods like breath-holding and navigator scans have limitations such as short acquisition times and complex implementations.
Innovation Solution
A system and method using a 1D navigator imaging sequence with a controller and MRI scanner to detect MR signals, identify motion cycles, and partition datasets into image groups, enabling motion-corrected MR images without external triggers or additional RF pulses, thereby improving image quality and reducing artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If breath-holding acquisition schemes are used to reduce motion-induced blurring, then image quality is improved, but acquisition time is limited to only tens of seconds and patient compliance becomes challenging
Solution Approach 1:
The system uses self-navigated MR imaging where the navigator signal is derived from the imaging data itself rather than external sensors. The method automatically detects motion cycles and partitions datasets into image groups corresponding to different respiratory phases, enabling free-breathing acquisition without requiring patient breath-holding compliance
Solution Approach 2:
The system implements feedback by using the navigator signal (derived from imaging data) to monitor respiratory motion in real-time and adjust the sorting of k-space data into respiratory phases. This closed-loop approach allows the system to adapt to patient breathing patterns dynamically during the scan
2Measurement precision
If respiratory-triggered methods with external sensors are used to track motion, then motion tracking capability is improved, but device complexity increases due to additional external sensors and signal contamination
Solution Approach 1:
The method extracts the navigator signal directly from the imaging data by analyzing k-space information, eliminating the need for external respiratory sensors. This extraction approach removes the complexity associated with external sensor placement, signal acquisition, and synchronization while avoiding contamination from external sensor signals
Solution Approach 2:
The imaging data serves multiple functions: it provides both the diagnostic image information and the navigator signal for motion tracking. This multi-functionality eliminates the need for separate external sensing systems and integrates motion tracking capability into the existing imaging sequence
3Loss of information
If conventional 4D MR imaging is used to capture respiratory motion, then motion information is obtained, but motion-induced artifacts and blurring reduce diagnostic effectiveness
Solution Approach 1:
The system segments the continuous k-space data into discrete respiratory phase groups based on navigator signal analysis. By partitioning datasets into image groups corresponding to specific respiratory phases (e.g., end-inspiration, end-expiration), the method creates motion-resolved images that clearly depict tumor position at different phases without the blurring artifacts of conventional 4D imaging
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
The approach provides high-quality, motion-corrected MR images that enhance diagnostic accuracy and treatment planning for lung cancer, while maintaining efficient data acquisition and reducing noise and artifacts, thus improving the effectiveness of MR imaging in clinical applications.
Implementation Method 1
magnetic resonance (MR) imaging
Data Source
AI summary
Systems and methods for producing motion-corrected MR images that include an MRI scanner with a plurality of coils in which the plurality of coils is configured to detect a plurality of MR imaging datasets are provided. Each MR imaging dataset includes a plurality of MR signals detected by one coil of the plurality of coils. The system further includes a controller operatively coupled to the MRI scanner that includes at least one processor and a non-volatile memory. The controller further includes an image processing unit configured to receive the plurality of MR imaging datasets associated with the plurality of coils, identify a motion cycle using an automated motion detection method, and to partition the plurality of MR imaging datasets into a plurality of image groups, in which each image group comprises a portion of the plurality of MR imaging datasets associated with one of the phases of the motion cycle.