Optimal Sensor Design Using Patient-Specific Images

Abstract

In diseases such as hydrocephalus, the cerebral ventricles enlarge. The treatment options for these patients are presently based on pressure, which has limited capabilities. We present the design of a volume sensor as an alternative monitoring option. Through the use of computer aided design and simulation, we optimized a sensor in silico with fewer resources. Specifically, we designed a sensor for animal experimentation with a scalable procedure for human sensors. In this paper, we present a rational design approach for a sensor that integrates advances in medical imaging. Magnetic resonance data sets of both normal and diseased subjects were used as a virtual laboratory. Finite element simulations were performed under pathological disease states of the brain as a contribution toward an accelerated device design. An optimized sensor was then fabricated for these subjects based on the outcome of the simulations. In this paper, we explain how a computer aided subject-specific design was used to help fabricate and test our sensor.