Advanced Computational and Deep Learning Methods for Image Reconstruction in Photoacoustic Computed Tomography

ABSTRACT

Photoacoustic computed tomography (PACT) is a rapidly developing hybrid-physics imaging modality with great potential for a wide range of biomedical applications. In PACT, a short laser pulse irradiates the object, leading to the absorption of optical energy. This absorbed energy induces a localized rise in acoustic pressure due to the photoacoustic effect, resulting in propagating acoustic waves that are subsequently measured by ultrasonic transducers. From these measurements, the initial pressure distribution can be estimated by use of an image reconstruction method. Image reconstruction in PACT faces several challenges. These include the need to mitigate the impact of spatially varying tissue acoustic properties and other modeling errors on image quality, and very large computational burdens. In this talk, we describe the development and application of advanced optimization-based and deep learning methods for image reconstruction that address these challenges, with a specific focus on breast imaging and transcranial neuroimaging using PACT.

BIOGRAPHY

Dr. Mark A. Anastasio is a Fellow of the IEEE, SPIE, American Institute for Medical and Biological Engineering (AIMBE), and International Academy of Medical and Biological Engineering (IAMBE). Dr. Anastasio’s research broadly addresses computational image science, tomographic image reconstruction, and the use of machine learning for applications in imaging science. He has made a wide range of contributions related to the computational aspects of wave-based imaging modalities that include optoacoustic/photoacoustic computed tomography and ultrasound computed tomography (UST). Dr. Anastasio has also been actively engaged in computational imaging science research related to the objective assessment of image quality and optimization of imaging system performance by use of modern machine learning methods. On these and other topics, he has published over 200 journal articles. Earlier in his career, he was the recipient of a National Science Foundation (NSF) CAREER Award to develop image reconstruction methods for X-ray phase-contrast tomography. He is a core member of the NIH-funded P41 Center for Label-free Imaging and Multiscale Biophotonics and leads the computational imaging research in that center. Dr. Anastasio’s direct mentoring experience includes 27 previous or current PhD students, 12 post-doctoral researchers, and 2 research faculty. His administrative accomplishments include serving as the primary architect and Founding Director of the PhD Program in Imaging Science at Washington University in St. Louis and overseeing the development of several new educational programs at UIUC that include two new undergraduate majors (neural engineering and a hybrid Bioengineering & Computer Science major), along with a new MS degree in biomedical image computing.