MRI Motion Detection Using Coil Array Signal Inconsistencies
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Solution Overview
Problem
Patient motion during MRI scans leads to inefficiencies, requiring re-scans or second visits, causing blurriness and artifacts in images, and existing methods either rely on costly hardware or time-consuming navigator sequences.
Innovation Solution
A method that uses inconsistencies in intensity-corrected single-coil images from a receiving coil array to detect and time patient motion, allowing for adaptive correction during or after the scan without external tracking hardware, using deep-learning neural networks or iterative optimization to correct for motion artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hardware for monitoring motion is added, then motion detection capability is improved, but device complexity and cost increase
Solution Approach 1:
The MRI system uses its own existing receiving coil array to detect patient motion by analyzing inconsistencies in the MR signals, eliminating the need for external motion monitoring hardware. The system serves itself by repurposing its primary imaging equipment for dual functions: image acquisition and motion detection.
Solution Approach 2:
The receiving coil array performs multiple functions: it serves as both the primary sensor for acquiring MR imaging data and as a motion detection sensor by analyzing signal inconsistencies. This multi-functionality eliminates the need for separate motion monitoring hardware while maintaining motion detection capability.
2Measurement precision
If navigator sequences are used for motion correction, then motion detection capability is improved, but scan time increases
Solution Approach 1:
The motion detection process is merged with the primary MR image acquisition process. By analyzing inconsistencies in the signals already being collected by the receiving coil array during the scan, the system detects motion without requiring separate navigator sequences or additional time-consuming measurements.
Solution Approach 2:
The system continuously monitors for motion throughout the entire scan duration by analyzing each signal acquisition in real-time, rather than periodically inserting separate motion assessment sequences. This continuous monitoring approach maintains scan efficiency while providing ongoing motion detection capability.
3Productivity
If motion is not corrected, then scan efficiency is maintained, but image quality deteriorates due to blurriness and artifacts
Solution Approach 1:
The system implements a feedback mechanism where motion is detected during the scan by analyzing signal inconsistencies, and this motion information is then used to correct the final image reconstruction. The feedback loop allows the system to maintain scan efficiency while improving image quality through post-processing correction based on detected motion patterns.
Solution Approach 2:
The system converts the harmful effect of patient motion into a detectable signal by analyzing inconsistencies in the MR data. Rather than viewing motion solely as a problem that requires scan interruption, the system uses the motion-induced signal variations as information to guide correction algorithms, ultimately producing motion-corrected images without requiring re-scans.
Data Source
AI summary
A system and method for detecting, timing, and adapting to patient motion during an MR scan includes using the inconsistencies between calculated images from different coil-array elements to detect the presence of patient motion and, together with the k-space scan-order information, determine the timing of the motion during the scan. Once the timing is known, various actions may be taken, including restarting the scan, reacquiring those portions of k-space acquired before the movement, or correcting for the motion using the existing data and reconstructing a motion-corrected image from the data.