Brain MRI Motion Monitoring for Precise SCC Target Mapping
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Solution Overview
Problem
Existing MRI data collection methods are hindered by head motion, leading to significant data loss and increased costs due to the need for overscanning to correct motion-related distortions, and current motion monitoring techniques are inadequate in distinguishing brain movements from facial or scalp movements.
Innovation Solution
Implementing real-time motion monitoring using Framewise Integrated Real-time MRI Monitoring (FIRMM) systems to identify and exclude motion-corrupted data, allowing for personalized patient-specific targeting of brain regions like the subcallosal cingulate (SCC) for neuromodulation, and adjusting scanning parameters based on motion feedback to optimize data quality and reduce costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frame censoring is used to remove motion-distorted data, then MRI data quality is improved, but data loss increases and scanning time increases due to overscanning
Solution Approach 1:
The patent applies preliminary action by monitoring head motion in real-time during MRI scanning and predicting future motion states before they cause significant data corruption. This allows the system to proactively adjust scanning parameters or exclude anticipated bad frames, preventing data loss before it occurs rather than reacting after contamination has happened.
Solution Approach 2:
The patent implements feedback by continuously monitoring head motion during scanning and using this information to dynamically adjust scanning parameters in real-time. The system receives feedback about motion status and modifies acquisition parameters accordingly, creating a closed-loop system that optimizes data quality while minimizing scanning time.
2Measurement precision
If frame censoring is used to remove motion-distorted data, then MRI data quality is improved, but the number of usable frames decreases
Solution Approach 1:
The system performs preliminary motion assessment and prediction to identify frames that will likely be corrupted before they are acquired. By predicting future motion states, the system can pre-planned adjustments to scanning parameters or selective frame exclusion, maximizing the retention of usable frames while maintaining data quality.
Solution Approach 2:
The patent changes scanning parameters dynamically based on motion conditions. When motion is detected, the system adjusts acquisition parameters such as scan speed, resolution, or timing to optimize the balance between data quality and frame retention, rather than using fixed parameters throughout the scan.
3Productivity
If real-time motion monitoring is implemented, then data quality is improved and scanning efficiency is optimized, but device complexity increases
Solution Approach 1:
The patent makes the motion monitoring system multi-functional by integrating it with existing MRI scanner components. The same hardware and software infrastructure used for image acquisition and processing is leveraged for motion monitoring and prediction, eliminating the need for separate dedicated systems and reducing overall complexity.
Solution Approach 2:
The system performs self-service by using the MRI scanner's own acquisition system and processing capabilities to monitor and predict head motion. Rather than requiring external monitoring equipment, the scanner uses its intrinsic resources (gradients, RF signals, and processing units) to accomplish motion monitoring, reducing device complexity.
4Measurement precision
If motion monitoring distinguishes brain movement from facial/scalp movement, then target identification accuracy is improved, but measurement precision requirements increase
Solution Approach 1:
The patent applies local quality by differentiating motion signals based on their spatial origin and characteristics. The system analyzes motion patterns in specific brain regions versus facial/scalp areas, using local anatomical knowledge and signal characteristics to distinguish between meaningful brain movement and artifact-related motion, improving target identification accuracy.
Solution Approach 2:
The system segments the head into distinct anatomical regions (brain, facial structures, scalp) and analyzes motion separately for each region. By dividing the complex motion problem into manageable spatial segments, the system can apply region-specific motion models and detection algorithms to accurately differentiate brain movement from other head movements.
Data Source
AI summary
A computer-implemented method for brain mapping and target identification for interventional planning using magnetic resonance imaging (MRI) includes receiving, by a computing system that includes at least one processor in communication with at least one memory system and that is in communication to receive data acquired using an MRI system, MR data from the MRI system. The method further includes analyzing the received MR data to monitor and identify motion in real-time, determining a set of useable MR data from the acquired MR data based on the identified motion, generating a map of the subject's brain based on the set of useable MR data and identifying a target location in the subcallosal cingulate (SCC) region of the subject's brain based on the map of the subject's brain. The target location can be a point of convergence of multiple fiber bundles passing through the SCC region. The method can further include generating a report indicating the target location.