The tools and techniques available for systems neuroscientists for neural recording and stimulation during behavior have become plentiful in the last decade. The tools for implementing these techniques in vivo, however, have not advanced respectively. The use of these techniques requires the removal of sections of skull tissue without damaging the underlying tissue, which is a very delicate procedure requiring significant training. Automating a part of the tissue removal processes would potentially enable more precise procedures to be performed, and it could democratize these procedres for widespread adoption by neuroscience lab groups. Here, we describe the ‘Craniobot’, a microsurgery platform that combines automated skull surface profiling with a computer numerical controlled (CNC) milling machine to perform a variety of microsurgical procedures in mice. Surface profiling by the Craniobot has micrometer precision, and the surface profiling information can be used to perform milling operations with relatively quick, allowing high throughput. We have used the Craniobot to perform skull thinning, small to large craniotomies, as well as drilling pilot holes for anchoring cranial implants. The Craniobot is implemented using open source and customizable machining practices and can be built with of the shelf parts for under $1000.
Skip Nav Destination
2018 Design of Medical Devices Conference
April 9–12, 2018
Minneapolis, Minnesota, USA
ISBN:
978-0-7918-4078-8
PROCEEDINGS PAPER
Principles of Computer Numerical Control Applied to Small Research Animal Surgical Procedures Free
Matthew Rynes,
Matthew Rynes
University of Minnesota, Minneapolis, MN
Search for other works by this author on:
Leila Ghanbari,
Leila Ghanbari
University of Minnesota, Minneapolis, MN
Search for other works by this author on:
Jay Jia Hu,
Jay Jia Hu
University of Minnesota, Minneapolis, MN
Search for other works by this author on:
Daniel Sousa Schulman,
Daniel Sousa Schulman
University of Minnesota, Minneapolis, MN
Search for other works by this author on:
Gregory Johnson,
Gregory Johnson
University of Minnesota, Minneapolis, MN
Search for other works by this author on:
Michael Laroque,
Michael Laroque
University of Minnesota, Minneapolis, MN
Search for other works by this author on:
Suhasa B. Kodandaramaiah
Suhasa B. Kodandaramaiah
University of Minnesota, Minneapolis, MN
Search for other works by this author on:
Matthew Rynes
University of Minnesota, Minneapolis, MN
Leila Ghanbari
University of Minnesota, Minneapolis, MN
Jay Jia Hu
University of Minnesota, Minneapolis, MN
Daniel Sousa Schulman
University of Minnesota, Minneapolis, MN
Gregory Johnson
University of Minnesota, Minneapolis, MN
Michael Laroque
University of Minnesota, Minneapolis, MN
Suhasa B. Kodandaramaiah
University of Minnesota, Minneapolis, MN
Paper No:
DMD2018-6959, V001T02A004; 3 pages
Published Online:
June 14, 2018
Citation
Rynes, M, Ghanbari, L, Hu, JJ, Schulman, DS, Johnson, G, Laroque, M, & Kodandaramaiah, SB. "Principles of Computer Numerical Control Applied to Small Research Animal Surgical Procedures." Proceedings of the 2018 Design of Medical Devices Conference. 2018 Design of Medical Devices Conference. Minneapolis, Minnesota, USA. April 9–12, 2018. V001T02A004. ASME. https://doi.org/10.1115/DMD2018-6959
Download citation file:
330
Views
Related Proceedings Papers
Related Articles
Custom TMJ Hemi-joint Fabrication Process
J. Med. Devices (June,2008)
Tool Sequence Selection for 2.5D Pockets with Uneven Stock
J. Comput. Inf. Sci. Eng (March,2006)
Dynamic Hybrid Modeling of the Vertical Z Axis in a High-Speed Machining Center: Towards Virtual Machining
J. Manuf. Sci. Eng (August,2007)
Related Chapters
Accuracy of an Axis
Mechanics of Accuracy in Engineering Design of Machines and Robots Volume I: Nominal Functioning and Geometric Accuracy
A Tool for Programming CNC Machining
International Conference on Instrumentation, Measurement, Circuits and Systems (ICIMCS 2011)
Fuzzy Neural Networks for Diagnosis of Malignant Mesothelioma
Intelligent Engineering Systems Through Artificial Neural Networks, Volume 17