Novel Implementation of Additive Manufacturing to Visualize the Human Brain Connectome

<p>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.</p> <p>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.</p> <p>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.</p>

Name thesis:degree_name

Master of Science (MS)

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computational and Data Sciences

Year dc:date.available

2023

Author dc:creator

• Tyler, Greg

Contributors dc:contributor

• Cyril Rakowski
• Adrian Vajiac
• Hagop Atamian

• 3D Printing
• Voxels
• Triangulated Mesh
• Interior Voxel Modeling
• Anatomy
• Biomedical Engineering and Bioengineering
• Medicine and Health Sciences
• Physical Sciences and Mathematics

Repository record dc:identifier

https://digitalcommons.chapman.edu/cads_theses/19

OAI identifier oai:identifier

oai:digitalcommons.chapman.edu:cads_theses-1018