Cranial Self-Registration for Wearable Ultrasound Brain Targeting
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Solution Overview
Problem
Existing focused ultrasound (FUS) systems for brain neuromodulation are impractical for home use due to the need for real-time MRI-based targeting and cannot correct for device migration during use, such as when encountering external structures, which affects beam targeting accuracy.
Innovation Solution
A neuromodulation system with cranial self-registration capability using imaging ultrasound elements and machine learning models to continuously derive updated beam focusing parameters, allowing accurate brain targeting despite device movement or displacement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional FUS systems use real-time MRI-based targeting, then beam targeting accuracy is improved, but device complexity and requirement for clinical supervision increase
Solution Approach 1:
The system performs self-registration by using the ultrasound transducer array to scan and map the patient's cranial anatomy automatically, eliminating the need for external MRI scanners or clinician intervention. The device autonomously derives beam focusing parameters from the scanned data, making the system self-sufficient for accurate brain targeting without continuous clinical supervision.
Solution Approach 2:
The patent replaces the mechanical/MRI-based targeting system with an ultrasound-based self-registration system. Instead of relying on external MRI scanners and manual positioning, the system uses ultrasound waves to scan the cranial anatomy and computationally determines the optimal beam focusing parameters, substituting a complex mechanical imaging system with a simpler ultrasound-based electronic system.
2Ease of operation
If FUS devices are used for home sleep enhancement, then ease of operation is improved, but device migration during use affects targeting accuracy
Solution Approach 1:
The system continuously monitors the actual position of the ultrasound transducer array relative to the patient's cranial anatomy using the self-registration mechanism. When device migration occurs, the system detects the position change and automatically updates the beam focusing parameters to compensate, maintaining accurate targeting throughout the night without requiring user intervention.
Solution Approach 2:
The system transitions from static pre-planned targeting to dynamic adaptive targeting. The beam focusing parameters are continuously updated based on real-time feedback about the device's actual position, allowing the system to adapt to device migration and maintain accuracy throughout the sleep treatment session.
3Measurement precision
If careful placement registration tools are used, then initial targeting accuracy is improved, but user experience becomes more burdensome
Solution Approach 1:
The system automatically performs the registration process by having the user simply wear the device and activate the self-registration function. The ultrasound array automatically scans the cranial anatomy and computes the optimal positioning, eliminating the need for users to manually adjust or position fiducial markers, thereby significantly improving ease of use while maintaining accuracy.
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
Maintains precise ultrasound beam targeting on brain regions like the centromedian nucleus of the thalamus, enhancing slow wave sleep and treating conditions like Parkinson's disease, without the need for continuous clinical supervision.
Implementation Method 1
A head-mounted array of ultrasound elements is used to acquire a reference volumetric dataset made of signals received by individual transducer elements (UVRef) during an initial CT or MRI scan
Implementation Method 2
The output displacements Ta are used to accurately simulate acoustic propagation for computing ultrasound focusing parameters in a neuromodulation device
Data Source
AI summary
A neuromodulation system is disclosed that comprises a neuromodulation device and a stimulation control computing environment. The disclosed device can include at least one ultrasound-emitting element. The stimulation control computing environment can be configured with data processing functions to focus ultrasound emission to a target brain region. The system can identify an initial position of the one or more ultrasound-emitting elements with respect to a temporal window of a user, use brain image to identify the target brain region, and perform first acoustic simulations to determine information for use in focusing ultrasound emissions from the initial position to the target brain region. The system can detect a shift of the one or more ultrasound-emitting elements with respect to the temporal window to a secondary position and perform second acoustic simulations to determine information for use in focusing ultrasound emissions from the secondary position to the target brain region.