{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129788"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129788","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Synthetic data generation pipeline to effectively train deep learning augmented super-resolution ultrasound imaging","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","abstract_has_math":false,"creators":["Katakam, Swathi"],"institution":"University of Illinois Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Chen, Yun-sheng"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-05-09","date_published":"2025-05-09","updated_at":"2026-07-22T22:25:05Z","subjects":["Super-resolution","Ultrasound","Synthetic Data","Microscopy"],"languages":["en","eng"],"rights":["Copyright 2025 Swathi Katakam"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129788","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chen, Yun-sheng"]},{"key":"dc:creator","label":"Author","values":["Katakam, Swathi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-05-09","2025-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"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 Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Super-resolution","Ultrasound","Synthetic Data","Microscopy"]}]},{"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 2025 Swathi Katakam"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129788"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","The student, Swathi Katakam, accepted the attached license on 2025-05-08 at 06:31.","The student, Swathi Katakam, submitted this Thesis for approval on 2025-05-08 at 06:43.","This Thesis was approved for publication on 2025-05-09 at 10:57.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22264 on 2025-10-19 at 19:55:51","Super-resolution ultrasound imaging is an emerging ultrasound technique capable of imaging microvascular structures and flow in unprecedented detail. Advancements in imaging speed and resolution are required to enable efficient 2D and 3D SRUI for clinical and preclinical applications. Deep learning methods are used to accelerate the acquisition and processing of SRUI datasets towards real-time imaging, but the efficacy of these methods is reliant on large quantities of high quality data. Synthetic data, typically generated moving microbubbles quasi-randomly in a 2-dimensional image is sometimes used to train these algorithms, but this method does not reliably mimic the vascular network and flow, which is 3-dimensional. The need for high quality 3D datasets as the field of 3D imaging advances. To address the gap in data availability, we present a workflow to generate large quantities of synthetic SRUI data from micro-CT angiographies of the mouse cerebrovascular network. Using image processing techniques we generate a realistic vector field through which we propagate the microbubbles through the vasculature. These microbubbles are gathered into frames which are simulated using ultrasound simulation software and subsequently analyzed to produce super-resolution ultrasound images similar in structure and flow to in-vivo datasets. This workflow advances current methods for generating synthetic data in SRUI by simplifying the simulation of microbubble flow in high-resolution whole-brain volumes."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Synthetic data generation pipeline to effectively train deep learning augmented super-resolution ultrasound imaging"]}]}],"canonical_facts":{"dc:contributor":["Chen, Yun-sheng"],"dc:creator":["Katakam, Swathi"],"dc:date":["2025-05-09","2025-05"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","The student, Swathi Katakam, accepted the attached license on 2025-05-08 at 06:31.","The student, Swathi Katakam, submitted this Thesis for approval on 2025-05-08 at 06:43.","This Thesis was approved for publication on 2025-05-09 at 10:57.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22264 on 2025-10-19 at 19:55:51","Super-resolution ultrasound imaging is an emerging ultrasound technique capable of imaging microvascular structures and flow in unprecedented detail. Advancements in imaging speed and resolution are required to enable efficient 2D and 3D SRUI for clinical and preclinical applications. Deep learning methods are used to accelerate the acquisition and processing of SRUI datasets towards real-time imaging, but the efficacy of these methods is reliant on large quantities of high quality data. Synthetic data, typically generated moving microbubbles quasi-randomly in a 2-dimensional image is sometimes used to train these algorithms, but this method does not reliably mimic the vascular network and flow, which is 3-dimensional. The need for high quality 3D datasets as the field of 3D imaging advances. To address the gap in data availability, we present a workflow to generate large quantities of synthetic SRUI data from micro-CT angiographies of the mouse cerebrovascular network. Using image processing techniques we generate a realistic vector field through which we propagate the microbubbles through the vasculature. These microbubbles are gathered into frames which are simulated using ultrasound simulation software and subsequently analyzed to produce super-resolution ultrasound images similar in structure and flow to in-vivo datasets. This workflow advances current methods for generating synthetic data in SRUI by simplifying the simulation of microbubble flow in high-resolution whole-brain volumes."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129788"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Swathi Katakam"],"dc:subject":["Super-resolution","Ultrasound","Synthetic Data","Microscopy"],"dc:title":["Synthetic data generation pipeline to effectively train deep learning augmented super-resolution ultrasound imaging"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:05Z"}