Computer Aided Sensor Design

1. E … A B 1 Day 7 Days 21 Days B C range Conductance (mS) A A B C E D range E D Control and Therapeutic Treatment Disease State Identification Voltage Drop Time (min) E D Power RF Transmitter 2 2 C Microcontroller Hydrocephalus B 1 1 3 3 Conductance (mS) Pump Pump A Normal 20 40 60 80 100 120 140 160 180 200 220 240 Volume (cc) Optimal Sensor Design and FabricationSukhraaj Basati, Timothy Harris and Andreas LinningerUIC Student Research Forum April 17th, 2009Laboratory for Product and Process Design, Dept. of Bioengineering University of Illinois at Chicago Hydrocephalus Case Study Motivation An abnormal accumulation of cerebrospinal fluid (CSF) leads to a condition known as Hydrocephalus. Over 150,000 people are diagnosed with this disease in the U.S. each year. CSF-Filled Lateral Ventricle Costs associated with treatment [1] Normal Patient Hydrocephalus Patient The current treatment method for all types of Hydrocephalus incorporates an intracranial pressure based shunt. Frequent problemsencountered with long-term intracranial pressure based shunts include: • Under-drainage or Over-drainage possibly leading to fatality • Multiple shunt revisions which require surgery • (The average lifespan of a shunt is five years) • For children: Failure rate = 50%. • For adults: Complication rate = 35 %. • Solution Approach: • Produce constant electric vector field via excitatory electrodes. • Changes in the electric field distribution occur with changes in volume. • Dependent on CSF and brain tissue electrical conductivity differences. • We have verified and implemented the principle on benchtop models [2]. Computer Aided Sensor Design Sensor Microfabrication 3D Microfabrication of electrodes onto catheter substrate • MRI from external collaborators [3] or patient specific images are reconstructed into 3D models (MIMICS). • Sensor is modeled with variable parameters MR Images • angle of sensor • length of sensor • number of electrodes • surface area of electrodes Image Reconstruction and 3D modeling Sensor design with geometrical constraints Sensor Optimization! Finite Element Simulation to analyze design Microfabrication based on FEA simulation • Decisions from simulations are implemented in the sensor fabrication. • Microfabrication techniques are performed at the Nanotechnology Core Facility at UIC. • Sensor Characteristics have to be tested via CV and EIS. • Other tests not shown include sensor stability, amplitude and frequency dependence. CAD of sensor • Decisions are made with respect to realizable, and biocompatible materials • CAD files are exported to FEA software (ADINA) for simulation and analysis. • Static simulations are performed with a normal size ventricle and enlarged ventricles based on patient data. • Dynamic simulations track the change in electric field distribution and provide knowledge of intracranial dynamics. • Instrumentation consists of sine wave generator, voltage – current converter, high input impedance op amps, a differential amplifier, an RMS to DC converter and a compact datalogger. 15 cc changes 5 cc changes Volume tracking may lead to proper control • Data is stored onto 2Gb microSD card for later analysis. FEA with design To Sensor Conclusions and Future Directions References Promising experimental and simulation results show that the conductance-volume relationship for Hydrocephalus patients can be used as a treatment option. [1] Patwardhan, R. “Implanted ventricular shunts in the United States: The Billion-Dollar-A-Year Cost of Hydrocephalus Treatment.” 2005, Neurosurgery, vol. 56, pp. 139-145 [2] A. Linninger, S. Basati, R. Dawe, R. Penn, An Impedance Sensor to Monitor and Control Cerebral Ventricular Volume., Medical Engineering and Physics, accepted 2009. [3] M.R. Del Bigio, C. Cook, R. Buist, “Magnetic Resonance Imaging and Behavioral Analysis of Immature Rats with Kaolin-Induced Hydrocephalus: Pre and Postshunting Observations.”, Experimental Neurology, 148 (1), pp. 256-264. [4] A. Linninger, M. Xenos, D. Zhu, M. Somayaji, R. Penn, “Cerebrospinal fluid flow in the normal and hydrocephalic brain”, IEEE Trans. Biomed. Eng., 54, 291-302, 2007. [5] D. Zhu, M. Xenos, A. Linninger, R. Penn, “Dynamics of Lateral Ventricle and Cerebrospinal Fluid in Normal and Hydrocephalic Brains.” Journal of Magnetic Resonance Imaging, 24, pp. 756-770, 2006. • Future Directions include: • Performing rigorous simulations to assess multiple sensor configurations and obtaining optimization algorithm for sensor design. • Acute animal experiments to validate principle in physiological systems. • Develop advanced sensors, including pressure measurements. • Perform long term in vivo study in animal models. Acknowledgments • Financial support provided for part of this research under NIH grant 5R21EB4956 is gratefully acknowledged. • The work was also supported by a grant from the STARS kid foundation. • This work is based on patent #WO/2008/005440 • We would also like to thank: • Dr. Richard Penn, University of Chicago • Dr. Del Bigio, University of Manitoba • Bob Lajos, Nanotechnology Core Facility manager at UIC • Brian Sweetman (LLPD member) • ADINA, inc. • Materialize, Inc.