Student Name: Prakash Manandhar
Email: pmanandhar@umassd.edu
Contact: (508)-441-9155
Research Topic: Printable sensor arrays for orthopedic rehabilitation
Advisor: Dr. Paul Calvert (Materials Science) and Dr. John Buck
(Electronics)
One of the frontiers of electronic materials science is to form flexible electronic devices that can continue to function despite large mechanical distortion. These systems have wide-ranging applications in various domains including biomedical engineering. For example, flexible electronic sensors could be integrated into artificial skins for prosthetic devices to enhance their function. Another application is for subjects to 'wear' the sensors to continuously monitor joint activity for rehabilitation of damaged joints or for athletic training. The current investigation includes optimizing the fabrication of sensor system using conducting polymer deposited on stretchable fabric material; understanding the mechanism of sensing and electronic signal processing from the sensor array to derive useful information from the array.
Research Topic: Intravascular Ultrasound Image Processing
Advisor: Dr. Chi Hau Chen
Intravascular Ultrasound (IVUS) has been used as a diagnostic tool to detect the presence of plaque in heart blood vessels and thus help to predict the possibility of a myocardial infarction before it occurs. Data from a single vessel in a single patient can consist of thousands of frames of images which need to be segmented into regions of vessel lumen, plaque and various layers in the vessel wall. The segmented data is then used to form a 3D geometry for computational fluid dynamic simulation to predict shear stresses on the vessel wall due to blood flow. The image segmentation process is a bottleneck because all other steps have been automated. Present automated segmentation algorithms are not accurate enough to drastically reduce the time required for manual segmentation. The current investigation includes developing new Active Contour Model based techniques to increase the accuracy of automated segmentation of IVUS data.
