Magee Abigail L, Svoboda Calamity, George Tessa G, Agato Alvin A, Richter Edward J, Culver Joseph P, Eggebrecht Adam T
Washington University, Department of Biomedical Engineering, St Louis, Missouri, United States.
Washington University, Mallinckrodt Institute of Radiology, School of Medicine, St Louis, Missouri, United States.
Neurophotonics. 2025 Jul;12(3):035002. doi: 10.1117/1.NPh.12.3.035002. Epub 2025 Jul 28.
Optical functional neuroimaging relies upon accurate anatomical models to provide optimal data registration and image reconstruction.
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).
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.
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.
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.
光学功能神经成像依赖于精确的解剖模型来实现最佳的数据配准和图像重建。
我们建立并验证一种用于头皮形态的稳健摄影测量算法，该算法利用带有摄影测量帽的三维（3D）成像，在没有磁共振成像（MRI）的情况下通过头发提供个性化的头皮形态估计。
头皮形态估计使用一个灵活的氯丁橡胶帽和带有基准点的3D打印摄影测量模块。我们创建稀疏的头皮采样并对齐MNI152图谱以生成头皮形态估计。我们使用国际10 - 20脑电图位置进行对齐，并将误差计算为基于表面的方法与特定参与者MRI之间修改后的10 - 5脑电图点子空间之间的欧几里得距离。
相对于特定参与者MRI的头皮形态估计误差，平均（标准差）误差为4.27（2.15）毫米，金标准体积配准为3.57（1.69）毫米，四点缩放图谱为11.45（6.00）毫米，未缩放图谱为5.35（1.52）毫米，无帽头皮估计为12.42（6.45）毫米。值得注意的是，头皮形态估计在误差的空间分布上显示出较低的方差，表明对特殊头部形状具有鲁棒性。
我们的头皮形态估计算法在有头发的情况下具有鲁棒性，无需MRI即可提供准确的特定参与者头部形状，并且可扩展到其他帽和基准点设计，从而突出了该工具在各种应用中的实用性。
