Advancing photoacoustic neuroimaging through 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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2022

Author dc:creator

• Kuo, Joseph

Contributors dc:contributor

• Anastasio, Mark A

• Photoacoustic
• Deep Learning

Statement dc:rights

• Copyright 2022 Joseph Kuo

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/117691

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F117691