Biological tissue can be quantitatively characterized by mechanical stiffness and electrical impedance. Such measurements are of use in diagnosing a variety of health and disease conditions, including preterm labor, cancer, and fibrosis. All systems available to date characterize one of these parameters, but primarily in excised tissues where neurovascular connections are severed. Furthermore, these devices are invasive and destructive, limiting their use in vivo to surface zones. We are building a novel, portable, easy-to-use, accurate device to do these combination measurements in the live human body non-destructively. It works like a minimally invasive biopsy needle, except the tissue is not harmed. Currently we have a working prototype and some preliminary data in live horses (an excellent model of pre-term birth), but much more is needed for the system to advance to marketability.
Specific MEng Goals:
We are looking for a team of MEng students to continue to refine the hardware and software of the system to decrease the time required per test and better automate the data analysis and reporting. Among these goals are the development of a rapid data sampling system using a Pic-32 microcontroller. We further want to use this device and collect biomechanical and impedance data for several tissue types (provided by clinical collaborators) as pilot studies for safety/efficacy analysis and potential market support. We hope to identify focus areas for which our device generates improved quantitative data without evidence of biological harm. . We would like to further improve the user interface.
Advisor Name: Jonathan Butcher
- Email - jtb47@cornell.edu
ECE Field Advisor Name: Bruce Land
- Email – brl4
- Phone – 255-7994
- Office – 214 Phillips
Interested candidates should email a statement of interest and suitability of background/training, an updated resume, and a fall semester class schedule to Jonathan Butcher (jtb47@cornell.edu). Teams of students are accepted. This project is being advertised to multiple departmental MEng programs. C# programming on Windows and C/Arduino on microcontroller, plus experience with high speed Direct Digital Synthesis and Analog-to-digital techniques is a plus.