Multi-parametric photoacoustic/ultrasound localization (PAUL) imaging and its applications

Noninvasive biomedical imaging technologies play a critical role in medical diagnostics, yet each modality has inherent limitations in spatial resolution, functional sensitivity, or molecular specificity. Hybrid imaging approaches have been developed to address these limitations, providing complementary structural and functional information for comprehensive tissue characterization. This thesis introduces a dual-modal photoacoustic and ultrasound localization (PAUL) imaging platform that integrates super-resolution ultrasound localization (UL) with photoacoustic (PA) imaging to enable multiparametric sensing of anatomical, structural, functional, and molecular features. We developed multiple PAUL imaging strategies to enhance its capabilities, including fast imaging, 3D imaging with a large field of view, on-demand contrast generation, and fully label-free imaging without exogenous agents. The multiparametric sensing capability makes PAUL imaging well-suited to capture complex biological disease processes and guide therapeutic interventions. We further demonstrated this capability through several applications: noninvasive monitoring of focused ultrasound–induced blood–brain barrier disruption, longitudinal assessment of renal microvascular dysfunction in acute kidney injury, and image-guided mechanochemical cancer therapy. Overall, this work establishes PAUL imaging as a high-resolution, versatile, and functional imaging platform for preclinical studies, offering new opportunities to probe vascular dynamics, tissue physiology, and therapeutic responses in vivo.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Zhao, Shensheng

Contributors dc:contributor

• Chen, Yun-Sheng
• Zhao, Yang
• Anastasio, Mark A
• Gruev, Viktor

• Super-resolution
• ultrasound imaging
• photoacoustic imaging
• functional imaging
• deep learning

Statement dc:rights

• Copyright 2025 Shensheng Zhao

Language dc:language

en

Handle dc:identifier

https://hdl.handle.net/2142/132766

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/132766