2026-09-21T04:55:18Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/115340 2023-07-11 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Shi, Humphrey Shi, Humphrey Allan Hasegawa-Johnson, Mark Liang, Zhi-Pei Sun, Shanhui 2022-05 application/pdf en text 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 Accurate and efficient cardiac motion estimation Yu, Hanchao 2022-02-25 cardiac motion estimation optical flow cardiac MR 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. Thesis eng https://hdl.handle.net/2142/115340 Copyright 2022 Hanchao Yu Ph.D. Dissertation Electrical & Computer Engr University of Illinois at Urbana-Champaign Electrical & Computer Eng