{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/115340"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/115340","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Accurate and efficient cardiac motion estimation","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms","abstract_has_math":false,"creators":["Yu, Hanchao"],"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":["Shi, Humphrey","Allan Hasegawa-Johnson, Mark","Liang, Zhi-Pei","Sun, Shanhui"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-05","date_published":"2022-05","updated_at":"2026-07-22T22:24:54Z","subjects":["cardiac motion estimation","optical flow","cardiac MR"],"languages":["en","eng"],"rights":["Copyright 2022 Hanchao Yu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/115340","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Shi, Humphrey","Allan Hasegawa-Johnson, Mark","Liang, Zhi-Pei","Sun, Shanhui"]},{"key":"dc:creator","label":"Author","values":["Yu, Hanchao"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-05","2022-02-25"]},{"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":["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":["cardiac motion estimation","optical flow","cardiac MR"]}]},{"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 Hanchao Yu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/115340"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms","The student, Hanchao Yu, accepted the attached license on 2022-02-24 at 17:37.","The student, Hanchao Yu, submitted this Dissertation for approval on 2022-02-24 at 17:48.","This Dissertation was approved for publication on 2022-02-25 at 10:10.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17511 on 2022-11-11 at 13:04:27","Cardiac motion estimation plays a key role in MRI cardiac feature tracking and function assessment such as myocardium strain. Recent research shows promising results with deep learning-based methods. However, in clinical deployment, previous methods suffer from several issues: (a) Significant performance drops due to mismatched distributions between training and testing datasets, commonly encountered in the clinical environment. It is difficult to collect all representative datasets and to train a universal tracker before deployment. (b) The searching space is large and the optimal is not unique due to the lack of ground truth motion field. (c) Existing deep learning-based methods are 2D models while the cardiac motion is 3D. In this thesis, we proposed a series of approaches to improve the efficiency and accuracy of cardiac motion estimation, along with new evaluation metrics: (a) We propose motion pyramid networks (MPN), a novel deep learning-based approach for accurate and efficient cardiac motion estimation. We predict and fuse a pyramid of motion fields from multiple scales of feature representations to generate a refined motion field. Progress motion compensation is proposed to improve the accuracy through multiple inferences. We then use a novel cyclic teacher-student training strategy to learn the compensation in a single inference step. New evaluation metrics are also proposed to represent errors in a clinically meaningful manner. (b) On top of MPN, we extend it to a novel model for 3D cardiac motion estimation. (c) We proposed a novel fast online adaptive learning (FOAL) framework for better performance on unseen data. It is an online gradient descent-based optimizer that is optimized by a meta-learner. The meta-learner enables the online optimizer to perform a fast and robust adaptation. Our proposed methods outperform strong baseline models on two public available clinical datasets, evaluated by a variety of metrics. The proposed methods also demonstrate time efficiency in inference and online adaptation."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Accurate and efficient cardiac motion estimation"]}]}],"canonical_facts":{"dc:contributor":["Shi, Humphrey","Allan Hasegawa-Johnson, Mark","Liang, Zhi-Pei","Sun, Shanhui"],"dc:creator":["Yu, Hanchao"],"dc:date":["2022-05","2022-02-25"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms","The student, Hanchao Yu, accepted the attached license on 2022-02-24 at 17:37.","The student, Hanchao Yu, submitted this Dissertation for approval on 2022-02-24 at 17:48.","This Dissertation was approved for publication on 2022-02-25 at 10:10.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17511 on 2022-11-11 at 13:04:27","Cardiac motion estimation plays a key role in MRI cardiac feature tracking and function assessment such as myocardium strain. Recent research shows promising results with deep learning-based methods. However, in clinical deployment, previous methods suffer from several issues: (a) Significant performance drops due to mismatched distributions between training and testing datasets, commonly encountered in the clinical environment. It is difficult to collect all representative datasets and to train a universal tracker before deployment. (b) The searching space is large and the optimal is not unique due to the lack of ground truth motion field. (c) Existing deep learning-based methods are 2D models while the cardiac motion is 3D. In this thesis, we proposed a series of approaches to improve the efficiency and accuracy of cardiac motion estimation, along with new evaluation metrics: (a) We propose motion pyramid networks (MPN), a novel deep learning-based approach for accurate and efficient cardiac motion estimation. We predict and fuse a pyramid of motion fields from multiple scales of feature representations to generate a refined motion field. Progress motion compensation is proposed to improve the accuracy through multiple inferences. We then use a novel cyclic teacher-student training strategy to learn the compensation in a single inference step. New evaluation metrics are also proposed to represent errors in a clinically meaningful manner. (b) On top of MPN, we extend it to a novel model for 3D cardiac motion estimation. (c) We proposed a novel fast online adaptive learning (FOAL) framework for better performance on unseen data. It is an online gradient descent-based optimizer that is optimized by a meta-learner. The meta-learner enables the online optimizer to perform a fast and robust adaptation. Our proposed methods outperform strong baseline models on two public available clinical datasets, evaluated by a variety of metrics. The proposed methods also demonstrate time efficiency in inference and online adaptation."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/115340"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Hanchao Yu"],"dc:subject":["cardiac motion estimation","optical flow","cardiac MR"],"dc:title":["Accurate and efficient cardiac motion estimation"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:54Z"}