Abbaszadeh's imaging work aims at assisting robotic neurosurgery
Dr. Shiva Abbaszadeh is collaborating with colleagues at Stanford University and in China to develop imaging, depth sensing, and machine learning techniques to aid physicians performing robotic neurosurgery on mice.
Abbaszadeh, an assistant professor in Nuclear, Plasma, and Radiological Engineering, has been awarded a one-year seed grant of $75,000 from the partnership of Zhejiang University with the University of Illinois at Urbana-Champaign Institute Research Program. Joining Abbaszadeh will be Dr. Hemmings Wu from Stanford’s Department of Neurosurgery, and Dr. Yong Lei from the Mechanical Engineering Department of Zhejiang University, one of China’s oldest and most prestigious institutions of higher education.
Small animal stereotaxic surgery is a common approach in small animal research to generate lesions, manipulate gene expression, or deliver experimental agents to the brain. Stereotactic surgery precisely directs the tip of a delicate instrument, such as a needle or beam of radiation, using a three-dimentional coordinate to reach a specific location.
Researchers working with the mice often use microscopes to determine where in the animals’ heads to inject tools to perform stereotactic surgeries. The lighting where the surgery takes place and the position and rotation of the animals’ heads can impact the procedure, which can be labor intensive and prone to error.
Abbaszadeh’s project, “Precision Robotic-assisted Implantation for Preclinical Stereotactic Neurosurgery,” aims to develop an application that will serve as a roadmap in assisting the surgeons. “We want to make something that is low-cost, and that they can use with a cell phone and algorithms,” she said.
Wu has provided Abbaszadeh and her graduate student, Zheng Liu, with over 200 images of mouse brain that she and Liu plan to use to train a machine-learning algorithm for the app.
“The algorithm should be able to identify a particular area that the surgeon wants to see, and will be able to work with new images based on learning from the previous images,” Abbaszadeh maintains.
“This method leverages a digital image capture system, artificial intelligence, and motorized stages,” she said. “Later in the project, we will leverage the expertise of Dr. Lei, from ZJU, in needle steering for the robot-assisted insertion system under development.
“Previously, Dr. Lei has worked on techniques to account for the needle-tissue interactions, which help guide the needle to accurately reach the target. The results of this work will be instrumental in securing additional funding to support the long-term goal of this project.”
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