{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/117691"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/117691","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Advancing photoacoustic neuroimaging through deep learning","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-12-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-12-01","abstract_has_math":false,"creators":["Kuo, Joseph"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Anastasio, Mark A"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-12","date_published":"2022-12","updated_at":"2026-07-22T22:24:56Z","subjects":["Photoacoustic","Deep Learning"],"languages":["en","eng"],"rights":["Copyright 2022 Joseph Kuo"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/117691","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Anastasio, Mark A"]},{"key":"dc:creator","label":"Author","values":["Kuo, Joseph"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-12","2022-12-09"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Photoacoustic","Deep Learning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2022 Joseph Kuo"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/117691"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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","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."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Advancing photoacoustic neuroimaging through deep learning"]}]}],"canonical_facts":{"dc:contributor":["Anastasio, Mark A"],"dc:creator":["Kuo, Joseph"],"dc:date":["2022-12","2022-12-09"],"dc:description":["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","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."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/117691"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Joseph Kuo"],"dc:subject":["Photoacoustic","Deep Learning"],"dc:title":["Advancing photoacoustic neuroimaging through deep learning"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:56Z"}