Notice and Invitation
Oral Defense of Doctoral Dissertation
The Volgenau School of Engineering, George Mason University
Bachelor of Science, Chemical Engineering, University of Virginia, 2013
Bachelor of Arts, Physics, University of Virginia, 2013
Master of Science, Biomedical Engineering, University of Virginia, 2015
Deep Learning for Sparse and Limited-View Photoacoustic Tomography Image Reconstruction
Thursday, September 30, 2021, 11:00am – 1:30pm
Peterson Hall 2000
All are invited to attend.
Dr. Parag V. Chitnis, Director
Dr. Qi Wei, Chair
Dr. Vadim Sokolov
Dr. Siddhartha Sikdar
Photoacoustic tomography (PAT) is a non-ionizing imaging modality capable of acquiring high contrast and resolution images based on optical absorption at depths greater than traditional optical imaging techniques. PAT has matured as a technology to the stage where transitioning from a laboratory to a clinical setting is possible. This presents a wide variety of practical considerations and limitations with instrumentation and data acquisition. Common challenges include having a limited number of available acoustic detectors and a reduced “view” of the imaging target which result in the acquisition of incomplete data. Forming an image with classical reconstruction methods from incomplete data result in image artifacts that degrade image quality. Advanced methods such as iterative reconstruction are effective in reducing and removing artifacts but are also computationally expensive and cannot be used for real-time imaging. Deep learning has the potential to be an effective and computationally efficient alternative to state-of-the-art iterative methods. Having such a method would enable improved image quality, real-time PAT image rendering, and more accurate image interpretation and quantification.