07/23/2026
By Marley O'Neil

The Francis College of Engineering, Department of Electrical and Computer Engineering, invites you to attend a Doctoral Dissertation defense by Sabrina Abedin titled: "Fiber-Optic Photoacoustic Sensing for Tissue Characterization: Probe Design, Mechanical Contrast Evaluation, and Machine Learning-Based Tumor-Phantom Detection."

Tuesday, August 4, 2026
8:30-10:30 a.m.
Perry Hall 215

Committee:

  • Advisor: Xingwei Wang, Professor, Electrical & Computer Engineering, UMass Lowell
  • Xuejun Lu, Professor, Electrical & Computer Engineering, UMass Lowell
  • Cordula Schmid, Associate Professor, Electrical & Computer Engineering, UMass Lowell
  • Joel Therrien, Associate Professor, Electrical & Computer Engineering, UMass Lowell
  • Jinxiang Xi, Associate Professor, Biomedical Engineering, UMass Lowell

Abstract:
All-optical ultrasound sensing is an emerging method for studying the properties of biological tissues and tissue-mimicking materials. Because diseased tissues often differ from healthy tissues in mechanical properties, acoustic impedance, optical absorption, and structural organization, photoacoustic signals can provide useful contrast for disease detection and tissue characterization. Fiber-optic photoacoustic probes are especially attractive because they are compact, immune to electromagnetic interference, and potentially suitable for minimally invasive sensing. However, probe-tip design, signal quality, and the interpretation of photoacoustic features remain important challenges for reliable tissue assessment. This study presents four fiber-tip geometries, including conical, wedge, angled, and flat tips, which were designed and characterized in the photoacoustic setup. Their performance was compared by scanning a vertebra phantom, demonstrating that fiber-tip geometry can significantly affect signal strength, spatial sensitivity, and imaging performance. The system was further used as an optical fiber-based photoacoustic system for evaluating the mechanical and structural properties of tissue-mimicking samples and phantoms.

The system was tested on several materials, including polydimethylsiloxane (PDMS), sawbones, bone, and tissue phantoms with different thicknesses and densities. Photoacoustic and acoustic reflection signals were analyzed and compared with compression-test measurements. The results showed that the measured effective mechanical response varied with sample density, thickness, and material composition, suggesting that the proposed method can help distinguish tissue-like materials based on mechanical contrast. In addition, in this study, an experimental analysis using layered tissue plates and biological phantoms is presented. It was able to detect different layers in muscle and abdominal-plate samples. A brain-tissue-mimicking phantom containing a small, embedded tube was then used to simulate a vessel-like structure behind tissue. The fiber-optic probe successfully distinguished the tube from the surrounding phantom and differentiated materials placed inside the tube. Finally, machine learning methods were applied to photoacoustic signals obtained from tissue phantoms containing tumor-like inclusions. Signal features were extracted and used to classify and identify tumor-embedded regions within the tissue phantom. The results demonstrate the potential of combining fiber-optic photoacoustic sensing, optimized probe design, and machine learning for tissue characterization and tumor-mimicking phantom detection. This dissertation provides a foundation for developing compact fiber-optic photoacoustic sensing systems for phantom-level tissue characterization, tumor-mimicking phantom detection, and image-guided tissue assessment.