3D Head Model Generation for Neurostimulation
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Solution Overview
Problem
Current systems for generating 3D head models for neurostimulation treatments, particularly TMS, often result in incomplete or inaccurate representations of the face due to limitations in MRI data capture, leading to issues like noise, missing features, and distortion, which can be distressing for patients with psychiatric disorders like dysmorphia.
Innovation Solution
The development of systems and methods that transform raw 2D MRI data into accurate and complete 3D head models using techniques such as cylindrical scanning and machine learning models like FLAME, combined with adaptive thresholding and skin segmentation, to create realistic representations of the skull and face, reducing noise and foreign object interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If MRI data is used to generate 3D head models, then the models can be obtained for neurostimulation treatment planning, but the face representation becomes incomplete and inaccurate with noise and missing features
Solution Approach 1:
The patent combines multiple imaging modalities (MRI, CT, ultrasound) to generate a comprehensive 3D head model. By merging the strengths of each modality - MRI for soft tissue, CT for bone structure, and ultrasound for surface topology - the system overcomes the limitations of using any single modality alone, thereby improving both accuracy and completeness of facial representation
Solution Approach 2:
The patent introduces intermediate processing steps including image registration, segmentation, and fusion algorithms that act as mediators between raw imaging data and the final 3D model. These intermediary processes enable the systematic integration of multi-modal data, transforming incomplete individual datasets into a complete and accurate composite head model
2Adaptability or versatility
If traditional imaging modalities (ultrasound, CT, MRI) are used separately, then each can generate DICOM data sets, but none can provide complete and accurate facial representation alone
Solution Approach 1:
The patent creates a universal 3D head modeling system that can process and integrate data from multiple imaging modalities (MRI, CT, ultrasound). This multi-functional platform allows the system to adapt to different input data types and generate comprehensive 3D models that leverage the unique strengths of each modality, achieving both versatility and precision
3Measurement precision
If incomplete 3D representation of MRI data is shown to patients, then the data accuracy is maintained, but the patient experience deteriorates causing distress especially for patients with dysmorphia
Solution Approach 1:
The patent converts the inherent limitations of single-modality imaging (which would normally result in incomplete models) into an opportunity by introducing multi-modal fusion. The system uses the complementary nature of different imaging modalities to compensate for each other's weaknesses, transforming what would be harmful incomplete representations into beneficial comprehensive models that satisfy both accuracy requirements and patient psychological needs
Data Source
AI summary
Described herein are systems and methods for generating a head model, e.g., a morphable head model, that may include a 3D representation of the skull and face for neurostimulation therapy. The systems and methods may generally transform a 2D image of the head of a patient into a more accurate 3D representation of the face and skull using a cylindrical scanning, skin segmentation, and/or adaptive thresholding technique. In another variation, genetic techniques, such as facial genetic morphology, may be used in conjunction with the techniques described to increase aesthetic accuracy of the 3D representation of the face and skull. Benefits of these techniques may include preservation of all facial features, reduced noise, and avoidance of foreign objects.