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Robust photogrammetric scalp morphology estimation for functional optical neuroimaging
Abigail L Magee1, Calamity Svoboda2, Tessa G George2
1Washington University, Department of Biomedical Engineering, St Louis, Missouri, United States.
Neurophotonics
|July 30, 2025
Summary
We developed a 3D imaging method using a special cap to accurately estimate scalp shape, even with hair, without needing MRI. This robust algorithm improves anatomical models for neuroimaging applications.
Area of Science:
- Neuroimaging
- Medical Imaging
- Biomedical Engineering
Background:
- Accurate anatomical models are crucial for optical functional neuroimaging.
- Current methods often rely on Magnetic Resonance Imaging (MRI), which can be costly and inaccessible.
- Individualized scalp morphology is essential for optimal data registration and image reconstruction.
Purpose of the Study:
- To establish and validate a robust photogrammetric algorithm for estimating individualized scalp morphology.
- To provide an accurate method for scalp shape estimation that does not require MRI.
- To develop a tool for improved anatomical modeling in neuroimaging.
Main Methods:
- Utilized 3-dimensional (3D) imaging with a specialized photogrammetric cap featuring fiducials.
- Employed a flexible neoprene cap for sparse scalp sampling.
- Aligned the MNI152 atlas using international 10-20 electroencephalogram positions for scalp morphology estimation.
Main Results:
- The photogrammetric cap method achieved a mean error of 4.27 (2.15) mm compared to participant-specific MRI.
- This method showed significantly lower error and variance than unscaled atlas or no-cap estimations.
- The algorithm demonstrated robustness to individual head shape variations.
Conclusions:
- The developed algorithm provides accurate, MRI-free scalp morphology estimation in the presence of hair.
- This technique offers a robust and scalable solution for creating individualized anatomical models.
- The tool has broad utility for various neuroimaging and cap-based applications.
