Creating anatomically derived, standardized, customizable, and three-dimensional printable head caps for functional neuroimaging

Abstract

Significance: Consistent and accurate probe placement is a crucial step toward enhancing the reproducibility of longitudinal and group-based functional neuroimaging studies. Although the selection of headgear is central to these efforts, there does not currently exist a standardized design that can accommodate diverse probe configurations and experimental procedures.

Aim: We aim to provide the community with an open-source software pipeline for conveniently creating low-cost, three-dimensional (3D) printable neuroimaging head caps with anatomically significant landmarks integrated into the structure of the cap.

Approach: We utilize our advanced 3D head mesh generation toolbox and 10-20 head landmark calculations to quickly convert a subject's anatomical scan or an atlas into a 3D printable head cap model. The 3D modeling environment of the open-source Blender platform permits advanced mesh processing features to customize the cap. The design process is streamlined into a Blender add-on named "NeuroCaptain."

Results: Using the intuitive user interface, we create various head cap models using brain atlases and share those with the community. The resulting mesh-based head cap designs are readily 3D printable using off-the-shelf printers and filaments while accurately preserving the head geometry and landmarks.

Conclusions: The methods developed in this work result in a widely accessible tool for community members to design, customize, and fabricate caps that incorporate anatomically derived landmarks. This not only permits personalized head cap designs to achieve improved accuracy but also offers an open platform for the community to propose standardizable head caps to facilitate multi-centered data collection and sharing.

Keywords: 10-20 system; 3D printing; electroencephalography; functional near-infrared spectroscopy; head cap; mesh generation; personalized medicine.

Fig. 1

General workflow of the NeuroCaptain cap design pipeline. Solid triangles indicate user inputs; yellow-shaded blocks are computed inside MATLAB/Octave.

Fig. 2

Diagram showing the process of incorporating 10-20 landmarks in a 3D printable cap model. The top half shows the steps computed in MATLAB/Octave; the bottom half shows the outputs in Blender.

Fig. 3

Workflow diagram illustrating the “geometry node” programming steps in Blender to convert 10-20 positions to surface grommets. The green-colored blocks indicate Blender geometry node functions. Overarching steps are specified by dotted lines encompassing the nodes.

Fig. 4

Diagram visualizing the data exchange between Blender and MATLAB/Octave environments, using a combination of Blender-Python application programming interface (API) and external MATLAB toolboxes (including Iso2Mesh and Brain2Mesh).

Fig. 5

Screenshot showing the NeuroCaptain GUI in Blender. Parameter dialog windows are shown in the middle allowing users to customize diverse cap settings. An animation showing the cap design process using NeuroCaptain can be accessed as Video 1 (Video 1, MP4, 6.80 MB [URL: https://doi.org/10.1117/1.NPh.12.1.015016.s1]).

Fig. 6

Sample head cap models generated by NeuroCaptain, including caps derived from (a) a 2-year-old atlas with coarse wireframe, (b) a 35- to 39-year-old atlas with dense wireframe, and 10-5 grommets, (c) an 80- to 84-year-old atlas with large diameter 10-10 markers, and (d) a 40- to 44-year-old atlas with thick wireframe and ear cut-out designs. The red circles denote the 10-20 landmarks embedded in the cap design.

Fig. 7

Sample 3D-printed caps. We first show the sliced cap model in panel (a) prepared for a multi-filament printer (Stratasys F170), with supporting materials shown in orange and the TPU cap shown in green. A photo of the partially printed cap on the Stratasys printer is shown in panel (b), and the completed cap after post-processing is shown in panel (c). A similar but dense wireframe cap was also printed and shown in panel (d). In panel (e), we show the sliced model prepared for a single-filament printer (Voron 2.4) with organic supports. Green indicates the organic support and orange indicates the actual cap, both printed with the same TPU material. The completed cap is shown in panel (f) with ear cut-outs. The 10-10 landmarks on all printed caps are painted red with acrylic paint to provide better visual guidance.

Fig. 8

Photos of (a) a 3D-printed head model created from the NDMRI 40-44 atlas and (b) donned with a head cap with pins inserted in the overlapping holes, indicating 10-20 positions. We also evaluate the accuracy of cap-embedded 10-20 positions measured over (c) a printed head model and (d) a human subject case study. Sagittal (blue circles) and coronal (red circles) landmarks are plotted. Ideally, the distances between the landmarks on the cap and head anatomy should be 1:1 ($y=x$), as depicted by the yellow dashed lines in panels (c) and (d). Each plotted point is labeled according to the 10-20 nomenclature.

Fig. 9

Validating cap 3D shape accuracy using photogrammetry. In panel (a), we show a 3D-textured surface recovered by DUST3r photogrammetry software; the 3D shape of the cap, including all wireframes and 10-10 markers, is reconstructed along with the subject’s head surface. In panel (b), we import the photogrammetry recovered surface into Blender showing good alignments with the originally designed 3D cap model (gray).

Fig. 10

Sample application of the 3D-printed head cap in fNIRS studies, showing (a) a cap carrying 10× modular optical brain imager (MOBI) modules placed over a subject’s head using locking pins and brackets, with a zoom-in view shown in panel (b), via the through-holes on the module and (c) the wearable fNIRS probe enclosed by a light-blocking fabric cap. The head cap is secured by a neck strap behind the head.