April 27, 2023

About the speaker

Dr. Jorge Cisneros Paz is currently a postdoctoral researcher in the Department of Biomedical Engineering at the University of Texas at Austin, focusing on projects related to artifact detection and clean-up in 4D CT scans and super-resolution photoacoustic image processing. He received his PhD degree in June 2022 from the Department of Applied Mathematics at the University of Washington, where he also earned his MS degree in 2019. His dissertation addresses the complications that arise in finite-difference methods with boundary conditions and ghost points when solving nonlinear problems. In 2017, Jorge received BS degrees in mathematics and physics with a minor in chemistry from the University of Texas Rio Grande Valley. Jorge’s research efforts focus on developing numerical techniques to solve problems that arise in all fields of STEM, ranging from dispersive phenomena to optimization methods to reduced-order methods. He has participated in many research programs since his undergraduate years, including a recent 10-month internship with the biotech company Genentech, and has presented at over 30 conferences with travel awards. In addition, Jorge holds a joint appointment with the National Science Foundation and the US Census Bureau on projects related to privacy data methods.

Machine learning pipeline for super-resolution photoacoustic imaging

Super-resolution photoacoustic (SR-PA) imaging has emerged as an attractive option for anatomical and functional imaging of microvasculature. However, the major bottleneck for advancing and translating the imaging approach is the lack of biocompatible and biodegradable particles that can serve as discrete photoacoustic sources when pulsed with near-infrared (NIR) light. In a recently submitted NIH grant, we propose to overcome this barrier by encapsulating self-assembled J-aggregates of indocyanine green (IJA) within giant unilamellar vesicles (IJA-GUV). The IJA-GUV serve as discrete photoacoustic sources at longer wavelengths than monomeric indocyanine green (ICG) (890 nm vs 780 nm, respectively), which allows for photoacoustic imaging at greater depths, and PEGylating the vesicles can lengthen the circulation time. In the proposed work, we will characterize the physicochemical and photoacoustic properties of the IJA-GUV and evaluate the achievable spatial resolution with the particles in vitro and in vivo. Upon completion of this exploratory grant, we anticipate having a contrast agent upon which clinical NIR SR-PA imaging can be developed. In this talk, we will present a machine learning pipeline for image processing of the vesicles that allows the reconstruction of microvasculature.