Accurate and efficient cardiac motion estimation

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2022

Author dc:creator

• Yu, Hanchao

Contributors dc:contributor

• Shi, Humphrey
• Allan Hasegawa-Johnson, Mark
• Liang, Zhi-Pei
• Sun, Shanhui

• cardiac motion estimation
• optical flow
• cardiac MR

Statement dc:rights

• Copyright 2022 Hanchao Yu

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/115340

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F115340