Robust photogrammetric scalp morphology estimation for functional optical neuroimaging.

Significance: Optical functional neuroimaging relies upon accurate anatomical models to provide optimal data registration and image reconstruction. Aim: We establish and validate a robust photogrammetric algorithm for scalp morphology that utilizes 3-dimensional (3D) imaging with a photogrammetric cap to provide individualized scalp morphology estimation through hair in the absence of magnetic resonance imaging (MRI). Approach: The scalp morphology estimation uses a flexible, neoprene cap and 3D-printed photogrammetric modules with fiducials. We create a sparse scalp sampling and align the MNI152 atlas to generate the scalp morphology estimation. We used the international 10 to 20 electroencephalogram positions for alignment and calculated the error as the Euclidean distance among a subspace of modified 10 to 5 electroencephalogram points between surface-based methods and participant-specific MRI. Results: The scalp morphology estimation error relative to participant-specific MRI had a mean (std) error of 4.27 (2.15) mm, a gold standard volumetric registration of 3.57 (1.69) mm, a four-point scaled atlas of 11.45 (6.00) mm, an unscaled atlas of 5.35 (1.52) mm, and a scalp estimation without cap of 12.42 (6.45) mm. Notably, the scalp morphology estimation demonstrated lower variance in the spatial distribution of error, indicating robustness to idiosyncratic head shapes. Conclusions: Our scalp morphology estimation algorithm is robust in the presence of hair, provides accurate participant-specific head shapes without requiring MRI, and scales to other cap and fiducial designs, thus highlighting the utility of this tool for a variety of applications.