{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/102440"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/102440","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A data-driven approach to quasi-static ultrasonic elasticity imaging","abstract":"\"Changes in the mechanical properties of soft tissues that accompany pathology can potentially be used as a biomarker for detection and diagnosis of disease. Quasi-static ultrasonic elastography (QUSE) is one of several elasticity imaging techniques sensitive to mechanical contrast and offers a way to visualize the spatiotemporal distribution of material properties within tissues. Unfortunately, QUSE is generally an ill-posed inverse problem. Quantifying the mechanical properties requires measurements of more stress-strain data than can be acquired during a typical ultrasonic (US) imaging exam. Model-based inverse methods attempt to circumvent these limitations in part by estimating the spatial distribution of a pre-defined set of material parameters. As a consequence, model-based methods provide no means for discovering new diagnostically-relevant mechanical properties or for exploring ranges of known model parameters for relevance in a given situation. We are developing a data-driven approach for quantitative QUSE using the Autoprogressive method (AutoP), which combines artificial neural networks (ANNs) and finite element analysis (FEA). AutoP has previously been used in geotechnical and civil engineering applications to build \"\"soft-computational\"\" models of materials. Using knowledge of object shape and force-displacement measurements, investigators were able to build neural network constitutive models (NNCMs) that accurately describe the behavior of linear, non-linear, and time-dependent materials with no prior constitutive model assumptions. Furthermore, NNCMs provide a means to estimate spatiotemporal stress and strain distributions from force-displacement data. NNCMs and AutoP offer a fundamentally different approach to QUSE. We first demonstrate that a very sparse sampling of force-displacement data is sufficient for estimating the linear-elastic properties of gelatin phantoms when the interior geometry is known. Then, we introduce Cartesian NNCMs (CaNNCMs), a novel ANN architecture, capable of learning both material property and geometric information. We begin exploring the spatial sampling requirements to reconstruct Young's modulus distributions in both 2-D and 3-D. Moreover, we show how CaNNCMs can be used to estimate the spatial distribution of all stresses and strains and can be directly interrogated to infer the mechanical properties governing measured data. Further development of this method to non-linear and viscoelastic materials may provide a means to discover the mechanical parameters most relevant to clinical elastography.\"","abstract_html":""Changes in the mechanical properties of soft tissues that accompany pathology can potentially be used as a biomarker for detection and diagnosis of disease. Quasi-static ultrasonic elastography (QUSE) is one of several elasticity imaging techniques sensitive to mechanical contrast and offers a way to visualize the spatiotemporal distribution of material properties within tissues. Unfortunately, QUSE is generally an ill-posed inverse problem. Quantifying the mechanical properties requires measurements of more stress-strain data than can be acquired during a typical ultrasonic (US) imaging exam. Model-based inverse methods attempt to circumvent these limitations in part by estimating the spatial distribution of a pre-defined set of material parameters. As a consequence, model-based methods provide no means for discovering new diagnostically-relevant mechanical properties or for exploring ranges of known model parameters for relevance in a given situation. We are developing a data-driven approach for quantitative QUSE using the Autoprogressive method (AutoP), which combines artificial neural networks (ANNs) and finite element analysis (FEA). AutoP has previously been used in geotechnical and civil engineering applications to build ""soft-computational"" models of materials. Using knowledge of object shape and force-displacement measurements, investigators were able to build neural network constitutive models (NNCMs) that accurately describe the behavior of linear, non-linear, and time-dependent materials with no prior constitutive model assumptions. Furthermore, NNCMs provide a means to estimate spatiotemporal stress and strain distributions from force-displacement data. NNCMs and AutoP offer a fundamentally different approach to QUSE. We first demonstrate that a very sparse sampling of force-displacement data is sufficient for estimating the linear-elastic properties of gelatin phantoms when the interior geometry is known. Then, we introduce Cartesian NNCMs (CaNNCMs), a novel ANN architecture, capable of learning both material property and geometric information. We begin exploring the spatial sampling requirements to reconstruct Young's modulus distributions in both 2-D and 3-D. Moreover, we show how CaNNCMs can be used to estimate the spatial distribution of all stresses and strains and can be directly interrogated to infer the mechanical properties governing measured data. Further development of this method to non-linear and viscoelastic materials may provide a means to discover the mechanical parameters most relevant to clinical elastography."","abstract_has_math":false,"creators":["Hoerig, Cameron"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Bioengineering","degree_department":null,"school":null,"contributors":["Insana, Michael F.","Ghaboussi, Jamshid","Boppart, Stephen","Sutton, Brad"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-02-06T19:36:04Z","date_published":"2019-02-06T19:36:04Z","updated_at":"2026-07-22T22:24:40Z","subjects":["Elasticity imaging","Inverse problems","Machine learning","Finite element analysis","Artificial neural networks"],"languages":["en"],"rights":["Copyright 2018 Cameron Hoerig"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/102440","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Insana, Michael F.","Ghaboussi, Jamshid","Boppart, Stephen","Sutton, Brad"]},{"key":"dc:creator","label":"Author","values":["Hoerig, Cameron"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-02-06T19:36:04Z","2018-11-26","2018-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Bioengineering"]},{"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":["Elasticity imaging","Inverse problems","Machine learning","Finite element analysis","Artificial neural networks"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2018 Cameron Hoerig"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/102440"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["\"Changes in the mechanical properties of soft tissues that accompany pathology can potentially be used as a biomarker for detection and diagnosis of disease. Quasi-static ultrasonic elastography (QUSE) is one of several elasticity imaging techniques sensitive to mechanical contrast and offers a way to visualize the spatiotemporal distribution of material properties within tissues. Unfortunately, QUSE is generally an ill-posed inverse problem. Quantifying the mechanical properties requires measurements of more stress-strain data than can be acquired during a typical ultrasonic (US) imaging exam. Model-based inverse methods attempt to circumvent these limitations in part by estimating the spatial distribution of a pre-defined set of material parameters. As a consequence, model-based methods provide no means for discovering new diagnostically-relevant mechanical properties or for exploring ranges of known model parameters for relevance in a given situation. We are developing a data-driven approach for quantitative QUSE using the Autoprogressive method (AutoP), which combines artificial neural networks (ANNs) and finite element analysis (FEA). AutoP has previously been used in geotechnical and civil engineering applications to build \"\"soft-computational\"\" models of materials. Using knowledge of object shape and force-displacement measurements, investigators were able to build neural network constitutive models (NNCMs) that accurately describe the behavior of linear, non-linear, and time-dependent materials with no prior constitutive model assumptions. Furthermore, NNCMs provide a means to estimate spatiotemporal stress and strain distributions from force-displacement data. NNCMs and AutoP offer a fundamentally different approach to QUSE. We first demonstrate that a very sparse sampling of force-displacement data is sufficient for estimating the linear-elastic properties of gelatin phantoms when the interior geometry is known. Then, we introduce Cartesian NNCMs (CaNNCMs), a novel ANN architecture, capable of learning both material property and geometric information. We begin exploring the spatial sampling requirements to reconstruct Young's modulus distributions in both 2-D and 3-D. Moreover, we show how CaNNCMs can be used to estimate the spatial distribution of all stresses and strains and can be directly interrogated to infer the mechanical properties governing measured data. Further development of this method to non-linear and viscoelastic materials may provide a means to discover the mechanical parameters most relevant to clinical elastography.\"","Submission original under an indefinite embargo labeled 'Open Access'. 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Quasi-static ultrasonic elastography (QUSE) is one of several elasticity imaging techniques sensitive to mechanical contrast and offers a way to visualize the spatiotemporal distribution of material properties within tissues. Unfortunately, QUSE is generally an ill-posed inverse problem. Quantifying the mechanical properties requires measurements of more stress-strain data than can be acquired during a typical ultrasonic (US) imaging exam. Model-based inverse methods attempt to circumvent these limitations in part by estimating the spatial distribution of a pre-defined set of material parameters. As a consequence, model-based methods provide no means for discovering new diagnostically-relevant mechanical properties or for exploring ranges of known model parameters for relevance in a given situation. We are developing a data-driven approach for quantitative QUSE using the Autoprogressive method (AutoP), which combines artificial neural networks (ANNs) and finite element analysis (FEA). AutoP has previously been used in geotechnical and civil engineering applications to build \"\"soft-computational\"\" models of materials. Using knowledge of object shape and force-displacement measurements, investigators were able to build neural network constitutive models (NNCMs) that accurately describe the behavior of linear, non-linear, and time-dependent materials with no prior constitutive model assumptions. Furthermore, NNCMs provide a means to estimate spatiotemporal stress and strain distributions from force-displacement data. NNCMs and AutoP offer a fundamentally different approach to QUSE. We first demonstrate that a very sparse sampling of force-displacement data is sufficient for estimating the linear-elastic properties of gelatin phantoms when the interior geometry is known. Then, we introduce Cartesian NNCMs (CaNNCMs), a novel ANN architecture, capable of learning both material property and geometric information. We begin exploring the spatial sampling requirements to reconstruct Young's modulus distributions in both 2-D and 3-D. Moreover, we show how CaNNCMs can be used to estimate the spatial distribution of all stresses and strains and can be directly interrogated to infer the mechanical properties governing measured data. Further development of this method to non-linear and viscoelastic materials may provide a means to discover the mechanical parameters most relevant to clinical elastography.\"","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2019-02-05 without embargo terms","The student, Cameron Hoerig, accepted the attached license on 2018-11-26 at 11:31.","The student, Cameron Hoerig, submitted this Dissertation for approval on 2018-11-26 at 11:40.","This Dissertation was approved for publication on 2018-11-26 at 14:53.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13114 on 2019-02-05 at 11:09:26","Made available in DSpace on 2019-02-06T19:36:04Z (GMT). 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