Date of Completion
5-4-2017
Embargo Period
5-3-2022
Advisors
Kazunori Hoshino, Guoan Zheng, Sabato Santaniello
Field of Study
Biomedical Engineering
Degree
Master of Science
Open Access
Campus Access
Abstract
Cerebrospinal fluid (CSF) is a body fluid contained in the brain ventricles and the cranial and spinal subarachnoid spaces. It plays an essential role in regulating neuronal function and maintaining homeostasis of interstitial fluid in the brain. Hydrocephalus is a neurological condition classified by the abnormal accumulation of cerebrospinal fluid (CSF) in the brain. It occurs due to a mismatch between CSF production and its absorption within the brain. The current preferred treatment for hydrocephalus is to post a CSF shunt system by medical procedure. However, shunt failure is relatively common among patients due to shunt malfunction, obstruction and infection.
This thesis proposes a microdevice to determine the shunt performance by measuring the flow rate in the compartment. The concept behind this microdevice is to establish a feedback system of measured conditions and analyzed data, providing parameters to examine shunt efficiency. The purpose of this microdevice is to improve current hydrocephalus treatment, assist neurologists to evaluate shunt performance, and reduce the chance of missed diagnosis of shunt failure. The microdevice operates based on the principle of bending cantilever brought by the pressure of the fluid in the shunt tube. The laser light was reflected by the beam inside the compartment, and then captured by a microscopic camera. The changes in light intensity revealed the flow rates in the shunt tube. In this study, we have designed and fabricated this novel sensor, and demonstrated its normal operation between 500 ml/hr and 900 ml/hr. The results also showed that the sensor can endure long-term pressure of the fluid without permeant deformation.
Recommended Citation
Bao, Mengdi, "A Novel Flow Sensor for a Smart Shunt System" (2017). Master's Theses. 1059.
https://digitalcommons.lib.uconn.edu/gs_theses/1059
Major Advisor
Kazunori Hoshino