03/27/2025
By Danielle Fretwell

The Francis College of Engineering, Department of Electrical and Computer Engineering, invites you to attend a Doctoral Dissertation Proposal defense by Sabrina Abedin on: "Characterization of Mechanical Properties of Tissues Using an Optical Fiber Photoacoustic System."

Date: Thursday, April 10, 2025
Time: 10 - 11:30 a.m.
Location: Perry 215

Committee:
Advisor: Xingwei Wang, Professor, Department of Electrical & Computer Engineering, UMass Lowell

Committee Members*
Xuejun Lu, Professor, Department of Electrical & Computer Engineering, UMass Lowell
Cordula Schmid, Associate Professor, Department of Electrical & Computer Engineering, UMass Lowell

Abstract:

Photoacoustic (PA) is an advanced technology used to evaluate properties of any sample, especially living cells for any life-threatening disease such as tumor, breast cancer, etc. Mechanical properties of the tissue will vary from normal healthy cells to disease-affected cells. This variation in mechanical properties such as Young’s Modulus variation can be determined using the Photoacoustic (PA) approach. As PA is a non-contact and non-invasive procedure, it is more comfortable to use for a patient as well as to characterize heterogeneous samples such as tissues. This study proposes an optical fiber generated photoacoustic system to determine Young’s Modulus for different kinds of tissues. This system is immune to electromagnetic interference, making the system a good fit to be used in tumor detection and monitoring purposes. This proposed system was used to successfully determine the Young’s Modulus of some materials such as Polydimethylsiloxane (PDMS) with different thickness, sawbones samples with different density. By analyzing the reflected signals from the target sample, Young’s Modulus was calculated and the results exhibited that Young’s Modulus varies for the same material but with different thickness and density. This result ensures that the proposed photoacoustic technique can also be used to classify different density tissues based on mechanical properties. Next, the tissue-like phantom will be used for differentiating the tumor cells. To classify and characterize the stage of the tumor, a machine learning algorithm will be used based on the experimental results from the samples.