2026-09-23T14:02:11Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/117691 2023-07-11 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Anastasio, Mark A 2022-12 application/pdf en text Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-12-01 The student, Joseph Kuo, accepted the attached license on 2022-12-09 at 11:32. The student, Joseph Kuo, submitted this Thesis for approval on 2022-12-09 at 11:38. This Thesis was approved for publication on 2022-12-09 at 13:11. DSpace SAF Submission Ingestion Package generated from Vireo submission #18793 on 2023-04-12 at 08:14:49 Advancing photoacoustic neuroimaging through deep learning Kuo, Joseph 2022-12-09 Photoacoustic Deep Learning Photoacoustic computed tomography (PACT) is a promising brain imaging modality in which the optically induced initial pressure distribution is reconstructed from the measured ultrasonic wavefields. Unlike x-ray computed tomography, PACT exposes the patient to no ionizing radiation. Computationally efficient image reconstruction algorithms have been developed using a homogeneous acoustic medium. However, this assumption is unwarranted in brain imaging due to the elastic and acoustic heterogeneities of the skull. To compensate for these heterogeneities, wave equation-based reconstruction algorithms have been developed based on the elastic finite-difference time-domain method. These methods yield high-quality images if the elastic and acoustic properties of the skull are known exactly. However, model-based reconstruction algorithms are generally computationally burdensome, making them ill-suited for functional imaging. To address these issues, we propose a two-step 3D reconstruction algorithm. The first step uses a computationally efficient but approximate image reconstruction algorithm. In the second step, a high-quality image is obtained by removing aberrations from the previous step using a 3-D convolutional neural network. The proposed approach is validated on computed simulation studies and compared with traditional model-based approaches. Thesis eng https://hdl.handle.net/2142/117691 Copyright 2022 Joseph Kuo M.S. Thesis Electrical & Computer Engr University of Illinois at Urbana-Champaign Electrical & Computer Eng