Speaker

Alycen Wiacek, PhD, Oakland University

Abstract

Medical imaging has benefited tremendously from the recent surge in data-driven approaches to image formation and analysis. Ultrasound and photoacoustic imaging are two related medical imaging modalities that transmit sound and light, respectively, into the body and measure signals representing the acoustic response from tissue. Images are created from these acoustic signals through the beamforming process. In this talk, I will highlight three applications leveraging data-driven methods to improve medical decisions and expand the availability of advanced algorithms into the clinic including: (1) coherence-based beamforming and its impact on breast imaging, (2) the development of a deep neural network architecture, named CohereNet, to improve the speed and availability of coherence-based beamforming, and (3) a novel auditory photoacoustic-based surgical guidance system for hysterectomy procedures. Together these applications demonstrate an important future for data-driven methods in ultrasound and photoacoustic imaging.

Biography

Alycen is an Assistant Professor at Oakland University in the Departments of Electrical and Computer Engineering and Bioengineering. She completed her PhD in Electrical Engineering at Johns Hopkins University as a member of the Photoacoustic and Ultrasonic Systems Engineering (PULSE) Lab studying ultrasound and photoacoustic imaging applied to women's health.

Her research focuses on ultrasound and photoacoustic imaging applied to radiology and surgical guidance. She also deploys advanced signal processing and deep learning methodologies to advance these fields and improve accessibility and impact of such advanced technologies. Through this work, her goal is to develop patient-centered, multidisciplinary solutions to imaging and medicine, together sculpting the future of equitable solutions for medical treatment.