Hybrid MEG-MRI Co-Registration via Calibration Phantom
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Solution Overview
Problem
Conventional co-registration procedures for magnetic resonance and magnetoencephalography data are prone to errors, leading to inaccuracies in localizing brain activity due to manual steps and external equipment requirements, which complicates the workflow and data analysis.
Innovation Solution
A hybrid MEG-MRI device that uses a mapping between the MRI image frame and sensor frame to achieve sensorwise agreement, allowing for calibration of the image and sensor frames, enabling accurate co-registration without manual intervention and external equipment, utilizing low-field or ultra-low-field magnetic resonance imaging to stabilize sensitivity profiles and model them accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional manual co-registration procedures are used to align MRI and MEG data, then the workflow can be completed with existing equipment, but co-registration errors up to 10 mm occur and workflow complexity increases
Solution Approach 1:
The patent combines MRI and MEG measurements into a single integrated system with shared sensors and coordinate systems. The hybrid device captures both anatomical (MRI) and functional (MEG) data simultaneously in the same sensor frame, eliminating the need for separate co-registration procedures and reducing alignment errors to sub-millimeter precision.
Solution Approach 2:
The patent introduces a calibration phantom as an intermediary object that contains both MRI-visible markers and MEG-detectable sources. This phantom serves as a common reference that enables automatic co-registration between the two modalities by providing corresponding features in both measurement types, thereby simplifying the alignment process.
2Measurement precision
If external calibration equipment is used to improve co-registration accuracy, then measurement precision improves, but device complexity and workflow complexity increase
Solution Approach 1:
The calibration phantom integrates both MRI and MEG measurement capabilities into a single device. It contains MRI-visible markers for anatomical reference and MEG-detectable magnetic sources for functional reference, allowing both modalities to be calibrated simultaneously using one unified equipment rather than separate calibration devices.
Solution Approach 2:
The calibration phantom serves multiple functions: it provides anatomical landmarks for MRI, functional sources for MEG, and a common reference frame for co-registration. This multi-functional design eliminates the need for separate calibration equipment for each modality, reducing overall system complexity while maintaining high precision.
3Measurement precision
If manual co-registration steps are performed to align different coordinate systems, then existing equipment can be used, but time consumption increases and accuracy is limited by manual error sources
Solution Approach 1:
The system performs automatic co-registration by utilizing the inherent geometric relationships between MRI markers and MEG sources in the calibration phantom. The processing algorithm automatically identifies corresponding features and computes the transformation matrix without requiring manual intervention, thereby eliminating human error sources and reducing processing time.
Solution Approach 2:
The calibration phantom is prepared in advance with known geometric relationships between its MRI markers and MEG sources. This pre-established reference framework allows the system to perform rapid automatic co-registration by simply matching the pre-known relationships, eliminating the need for time-consuming manual alignment procedures during actual measurements.
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
This approach improves the accuracy of brain activity localization by reducing co-registration errors, providing a co-registration-free workflow and enabling precise alignment of MRI and MEG data, thus enhancing the precision of neuronal field modeling and source localization.
Implementation Method 1
generating Low-Field-Magnetic Resonance Imaging, LF-MRI, or Ultra-Low-Field Magnetic Resonance Imaging, ULF-MRI, data
Implementation Method 2
sensors arranged at positions around an imaged target volume
Data Source
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AI summary
According to an example aspect of the present invention, there is provided generating, Low-Field-Magnetic Resonance Imaging, LF-MRI, or Ultra-Low-Field Magnetic Resonance Imaging, ULF-MRI, data with respect to an image frame, determining a sensor wise agreement of the data with determined sensitivity profiles, and determining a mapping between the image frame and a sensor frame,such that the sensor wise agreement has been fulfilled.