Novel Implementation of Additive Manufacturing to Visualize the Human Brain Connectome

Author

Greg Tyler, Chapman UniversityFollow

Date of Award

Fall 12-2023

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computational and Data Sciences

First Advisor

Cyril Rakowski

Second Advisor

Adrian Vajiac

Third Advisor

Hagop Atamian

Abstract

Additive manufacturing (AM) is an emerging technology with diverse areas of application. In this paper we explore a new application of AM that uses a subset of AM known as 3D printing, to create real life models of the human connectome. The human connectome is a color coded map of the connections in the human brain where directions are indicated by colors and the density of connections are indicated by color intensity.

There are several different algorithms for mapping the connections and describing the output. The Neu- roImaging Tools and Resource Collaboratory (NITRC) provides one such algorithm. It uses probabilistic mapping on an MRI to identify neural pathways and averages these identified connections from many MRI’s to create a standard atlas of the adult human brain.

Currently AM files for 3D printing rely on a surface triangulation mesh to describe the model. This mesh consists of a series of vertices and faces, each one describing the location of a triangle in 3D space. Each triangle shares two vertices with another triangle. A continuous mesh is created by combining a set of such triangles that can represent irregular surfaces. These meshes however do not allow for interior points or interior color data. In this work we describe a novel solution for creating voxels from surface triangulation that allows the atlas to be directly translated to an accurate 3D printed model.

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Recommended Citation

G. Tyler, "Novel implementation of additive manufacturing to visualize the human brain connectome," M. S. thesis, Chapman University, Orange, CA, 2023. https://doi.org/10.36837/chapman.000521