{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/105027"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/105027","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Toward model-free and reference-free quantitative ultrasound","abstract":"Current spectral-based quantitative ultrasound (QUS) techniques rely on a reference or a calibration signal to obtain system-independent ultrasonic scattering parameters. Adoption of QUS clinically has been hindered by (1) the need to limit clinical ultrasonic scanner settings to account for a finite number of calibration signals or (2) the need to acquire calibration signals immediately before or after a setting is changed on an ultrasonic scanner, which can interrupt the workflow in the busy clinical environment. An additional factor that hinders the effectiveness of QUS clinically is the presence of layers over the tissue regions to be interrogated. Layer effects are not accounted for when using a reference phantom, reducing the technique's reliability. The dissertation presents experimental and computational approaches to improve the accuracy of QUS estimates in the presence of intervening layers using an in situ calibration target and the feasibility of reference-free quantitative ultrasound using a convolutional neural network. The sensitivity of CNN in a reference-free environment is assessed and experimentally validated. A model-free approach through the use of principal component analysis is proposed as a general method for tissue characterization using QUS and compared to a model-based approach. To address the effects of intervening layers on QUS accuracy and precision, an in situ calibration approach is proposed and experimentally verified to improve the QUS estimates.","abstract_html":"Current spectral-based quantitative ultrasound (QUS) techniques rely on a reference or a calibration signal to obtain system-independent ultrasonic scattering parameters. Adoption of QUS clinically has been hindered by (1) the need to limit clinical ultrasonic scanner settings to account for a finite number of calibration signals or (2) the need to acquire calibration signals immediately before or after a setting is changed on an ultrasonic scanner, which can interrupt the workflow in the busy clinical environment. An additional factor that hinders the effectiveness of QUS clinically is the presence of layers over the tissue regions to be interrogated. Layer effects are not accounted for when using a reference phantom, reducing the technique's reliability. The dissertation presents experimental and computational approaches to improve the accuracy of QUS estimates in the presence of intervening layers using an in situ calibration target and the feasibility of reference-free quantitative ultrasound using a convolutional neural network. The sensitivity of CNN in a reference-free environment is assessed and experimentally validated. A model-free approach through the use of principal component analysis is proposed as a general method for tissue characterization using QUS and compared to a model-based approach. To address the effects of intervening layers on QUS accuracy and precision, an in situ calibration approach is proposed and experimentally verified to improve the QUS estimates.","abstract_has_math":false,"creators":["Nguyen, Trong Ngoc"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Oelze, Michael L.","Do, Minh N.","Boppart, Stephen A.","Liang, Zhi-Pei"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-08-23T20:35:54Z","date_published":"2019-08-23T20:35:54Z","updated_at":"2026-07-22T22:24:44Z","subjects":["Quantitative ultrasound, reference-free, fatty liver, in situ calibration"],"languages":["en"],"rights":["Copyright 2019 Trong Nguyen"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/105027","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Oelze, Michael L.","Do, Minh N.","Boppart, Stephen A.","Liang, Zhi-Pei"]},{"key":"dc:creator","label":"Author","values":["Nguyen, Trong Ngoc"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-08-23T20:35:54Z","2021-08-24T09:15:16Z","2019-04-16","2019-05"]},{"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":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"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":["Quantitative ultrasound, reference-free, fatty liver, in situ calibration"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Trong Nguyen"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/105027"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Current spectral-based quantitative ultrasound (QUS) techniques rely on a reference or a calibration signal to obtain system-independent ultrasonic scattering parameters. Adoption of QUS clinically has been hindered by (1) the need to limit clinical ultrasonic scanner settings to account for a finite number of calibration signals or (2) the need to acquire calibration signals immediately before or after a setting is changed on an ultrasonic scanner, which can interrupt the workflow in the busy clinical environment. An additional factor that hinders the effectiveness of QUS clinically is the presence of layers over the tissue regions to be interrogated. Layer effects are not accounted for when using a reference phantom, reducing the technique's reliability. The dissertation presents experimental and computational approaches to improve the accuracy of QUS estimates in the presence of intervening layers using an in situ calibration target and the feasibility of reference-free quantitative ultrasound using a convolutional neural network. The sensitivity of CNN in a reference-free environment is assessed and experimentally validated. A model-free approach through the use of principal component analysis is proposed as a general method for tissue characterization using QUS and compared to a model-based approach. To address the effects of intervening layers on QUS accuracy and precision, an in situ calibration approach is proposed and experimentally verified to improve the QUS estimates.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-05-01","The student, Trong Nguyen, accepted the attached license on 2019-04-15 at 14:20.","The student, Trong Nguyen, submitted this Dissertation for approval on 2019-04-15 at 14:29.","This Dissertation was approved for publication on 2019-04-16 at 08:25.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13632 on 2019-08-22 at 15:06:26","Made available in DSpace on 2019-08-23T20:35:54Z (GMT). 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Adoption of QUS clinically has been hindered by (1) the need to limit clinical ultrasonic scanner settings to account for a finite number of calibration signals or (2) the need to acquire calibration signals immediately before or after a setting is changed on an ultrasonic scanner, which can interrupt the workflow in the busy clinical environment. An additional factor that hinders the effectiveness of QUS clinically is the presence of layers over the tissue regions to be interrogated. Layer effects are not accounted for when using a reference phantom, reducing the technique's reliability. The dissertation presents experimental and computational approaches to improve the accuracy of QUS estimates in the presence of intervening layers using an in situ calibration target and the feasibility of reference-free quantitative ultrasound using a convolutional neural network. The sensitivity of CNN in a reference-free environment is assessed and experimentally validated. A model-free approach through the use of principal component analysis is proposed as a general method for tissue characterization using QUS and compared to a model-based approach. To address the effects of intervening layers on QUS accuracy and precision, an in situ calibration approach is proposed and experimentally verified to improve the QUS estimates.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-05-01","The student, Trong Nguyen, accepted the attached license on 2019-04-15 at 14:20.","The student, Trong Nguyen, submitted this Dissertation for approval on 2019-04-15 at 14:29.","This Dissertation was approved for publication on 2019-04-16 at 08:25.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13632 on 2019-08-22 at 15:06:26","Made available in DSpace on 2019-08-23T20:35:54Z (GMT). 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