{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/109605"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/109605","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Advancing the intravoxel incoherent motion model as a tool for quantifying brain microvascular health","abstract":"Quantification of the state of the microvasculature of the human brain is currently only feasible via invasive histological methods using light microsocopy. In order to correlate the state of the microvasculature of the brain with functional measures such as blood flow or cognition in humans, noninvasive techniques are required. One model for measuring microvascular blood flow is the intravoxel incoherent motion (IVIM) model of pseudodiffusive blood flow. In this work, a set of IVIM-derived microvascular biomarkers are pro- posed to quantify the mean microvascular blood velocity and mean microvessel length via diffusion weighted imaging (DWI). Very long mixing time stimulated acquisition mode diffusion weighted magnetic resonance imaging pulse sequences were developed and implemented to acquire the long diffusion time data required to derive the proposed microvascular biomarkers. To accelerate the computationally burdensome iterative reconstructions used in this work, a novel GPU accelerated toolkit, PowerGrid, was developed to implement the reconstructions. Speed ups in excess of 17x were obtained via PowerGrid on commercial GPUs. To address the challenges of fitting IVIM parameters reliably, two strategies were examined. The first was the application of a combination diffusion tensor-IVIM model as applied to white matter and deep gray matter in the human brain. The second was a novel estimator using a reducing variance at the cost of increased bias by applying bootstrap aggregation from field of machine learning. The bootstrap aggregation estimator was used in a hypercapnia paradigm to attempt to demonstrate the proposed biomarkers in vivo. Finally, future work towards further validating and developing the proposed biomarkers in humans is described.","abstract_html":"Quantification of the state of the microvasculature of the human brain is currently only feasible via invasive histological methods using light microsocopy. In order to correlate the state of the microvasculature of the brain with functional measures such as blood flow or cognition in humans, noninvasive techniques are required. One model for measuring microvascular blood flow is the intravoxel incoherent motion (IVIM) model of pseudodiffusive blood flow. In this work, a set of IVIM-derived microvascular biomarkers are pro- posed to quantify the mean microvascular blood velocity and mean microvessel length via diffusion weighted imaging (DWI). Very long mixing time stimulated acquisition mode diffusion weighted magnetic resonance imaging pulse sequences were developed and implemented to acquire the long diffusion time data required to derive the proposed microvascular biomarkers. To accelerate the computationally burdensome iterative reconstructions used in this work, a novel GPU accelerated toolkit, PowerGrid, was developed to implement the reconstructions. Speed ups in excess of 17x were obtained via PowerGrid on commercial GPUs. To address the challenges of fitting IVIM parameters reliably, two strategies were examined. The first was the application of a combination diffusion tensor-IVIM model as applied to white matter and deep gray matter in the human brain. The second was a novel estimator using a reducing variance at the cost of increased bias by applying bootstrap aggregation from field of machine learning. The bootstrap aggregation estimator was used in a hypercapnia paradigm to attempt to demonstrate the proposed biomarkers in vivo. Finally, future work towards further validating and developing the proposed biomarkers in humans is described.","abstract_has_math":false,"creators":["Cerjanic, Alexander"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Bioengineering","degree_department":null,"school":null,"contributors":["Sutton, Bradley P","Liang, Zhi-Pei","Cohen, Neal","Llano, Daniel"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-03-05T21:45:39Z","date_published":"2021-03-05T21:45:39Z","updated_at":"2026-07-22T22:24:50Z","subjects":["Magnetic Resonance Imaging","Diffusion weighted imaging","microvascular blood flow","intravoxel incoherent motion","IVIM"],"languages":["en"],"rights":["Copyright 2020 Alexander Cerjanic"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/109605","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Sutton, Bradley P","Liang, Zhi-Pei","Cohen, Neal","Llano, Daniel"]},{"key":"dc:creator","label":"Author","values":["Cerjanic, Alexander"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021-03-05T21:45:39Z","2023-03-05T21:47:41Z","2020-12-02","2020-12"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"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":["Magnetic Resonance Imaging","Diffusion weighted imaging","microvascular blood flow","intravoxel incoherent motion","IVIM"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Alexander Cerjanic"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/109605"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Quantification of the state of the microvasculature of the human brain is currently only feasible via invasive histological methods using light microsocopy. In order to correlate the state of the microvasculature of the brain with functional measures such as blood flow or cognition in humans, noninvasive techniques are required. One model for measuring microvascular blood flow is the intravoxel incoherent motion (IVIM) model of pseudodiffusive blood flow. In this work, a set of IVIM-derived microvascular biomarkers are pro- posed to quantify the mean microvascular blood velocity and mean microvessel length via diffusion weighted imaging (DWI). Very long mixing time stimulated acquisition mode diffusion weighted magnetic resonance imaging pulse sequences were developed and implemented to acquire the long diffusion time data required to derive the proposed microvascular biomarkers. To accelerate the computationally burdensome iterative reconstructions used in this work, a novel GPU accelerated toolkit, PowerGrid, was developed to implement the reconstructions. Speed ups in excess of 17x were obtained via PowerGrid on commercial GPUs. To address the challenges of fitting IVIM parameters reliably, two strategies were examined. The first was the application of a combination diffusion tensor-IVIM model as applied to white matter and deep gray matter in the human brain. The second was a novel estimator using a reducing variance at the cost of increased bias by applying bootstrap aggregation from field of machine learning. The bootstrap aggregation estimator was used in a hypercapnia paradigm to attempt to demonstrate the proposed biomarkers in vivo. Finally, future work towards further validating and developing the proposed biomarkers in humans is described.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2022-12-01","The student, Alexander Cerjanic, accepted the attached license on 2020-11-28 at 12:57.","The student, Alexander Cerjanic, submitted this Dissertation for approval on 2020-11-30 at 18:53.","This Dissertation was approved for publication on 2020-12-02 at 07:48.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15975 on 2021-03-04 at 16:32:38","Made available in DSpace on 2021-03-05T21:45:39Z (GMT). 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In order to correlate the state of the microvasculature of the brain with functional measures such as blood flow or cognition in humans, noninvasive techniques are required. One model for measuring microvascular blood flow is the intravoxel incoherent motion (IVIM) model of pseudodiffusive blood flow. In this work, a set of IVIM-derived microvascular biomarkers are pro- posed to quantify the mean microvascular blood velocity and mean microvessel length via diffusion weighted imaging (DWI). Very long mixing time stimulated acquisition mode diffusion weighted magnetic resonance imaging pulse sequences were developed and implemented to acquire the long diffusion time data required to derive the proposed microvascular biomarkers. To accelerate the computationally burdensome iterative reconstructions used in this work, a novel GPU accelerated toolkit, PowerGrid, was developed to implement the reconstructions. Speed ups in excess of 17x were obtained via PowerGrid on commercial GPUs. To address the challenges of fitting IVIM parameters reliably, two strategies were examined. The first was the application of a combination diffusion tensor-IVIM model as applied to white matter and deep gray matter in the human brain. The second was a novel estimator using a reducing variance at the cost of increased bias by applying bootstrap aggregation from field of machine learning. The bootstrap aggregation estimator was used in a hypercapnia paradigm to attempt to demonstrate the proposed biomarkers in vivo. Finally, future work towards further validating and developing the proposed biomarkers in humans is described.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2022-12-01","The student, Alexander Cerjanic, accepted the attached license on 2020-11-28 at 12:57.","The student, Alexander Cerjanic, submitted this Dissertation for approval on 2020-11-30 at 18:53.","This Dissertation was approved for publication on 2020-12-02 at 07:48.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15975 on 2021-03-04 at 16:32:38","Made available in DSpace on 2021-03-05T21:45:39Z (GMT). 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