Abstract

Image-based patient-specific anatomical models of the heart have the potential to be used in a variety of clinical scenarios such as diagnosis and prognosis of various cardiovascular diseases (CVDs), including cardiac resynchronization therapy (CRT), ablation therapy, risk stratification, and minimally invasive cardiac interventions. Cardiac magnetic resonance imaging (MRI) provides images with high-resolution and superior soft tissue contrast, rendering it as the gold standard modality for imaging cardiac anatomy. To obtain meaningful information from such image-based personalized anatomical models of the heart, it is crucial to combine the geometric models of the cardiac chambers extracted from cine cardiac MRI and the scar anatomy from the late gadolinium enhanced (LGE) MRI. There are several challenges to be tackled to generate patient-specific anatomical models of the heart from the cardiac MRI data. Firstly, accurate and robust automated segmentation of the cardiac chambers from the cine cardiac MRI data is essential to estimate cardiac function indices. Secondly, it is important to estimate cardiac motion from 4D cine MRI data to assess the kinematic and contractile properties of the myocardium. Thirdly, accurate registration of the LGE MRI images with their corresponding cine MRI images is crucial to assess myocardial viability. In addition to the above-mentioned segmentation and registration tasks, it is also crucial to computationally super-resolve the anisotropic (high in-plane and low through-plane resolution) cardiac MRI images, while maintaining the structural integrity of the tissues. With the advent of deep learning, medical image segmentation and registration have immensely benefited. In this work, we present a deep learning-based framework to generate personalized cardiac anatomical models using cardiac MRI data. Firstly, we segment the cardiac chambers from an open-source cine cardiac MRI data using an adversarial deep learning framework. We evaluate the viability of the proposed adversarial framework by assessing its effect on the clinical cardiac parameters. Secondly, we propose a convolutional neural network (CNN) based 4D deformable registration algorithm for cardiac motion estimation from an open-source 4D cine cardiac MRI dataset. We extend this proposed CNN-based 4D deformable registration algorithm to develop dynamic patient-specific geometric models of the left ventricle (LV) myocardium and right ventricle (RV) endocardium. Thirdly, we present a deep learning framework for registration of cine and LGE MRI images, and assess the registration performance of the proposed method on an open source dataset. Finally, we present a 3D CNN-based framework with structure preserving gradient guidance to generate super-resolution cardiac MRI images, and assess this proposed super-resolution algorithm on an open-source LGE MRI dataset. Furthermore, we investigate the effect of the proposed super-resolution algorithm on downstream segmentation task.

Library of Congress Subject Headings

Heart--Models; Heart--Ventricles--Imaging; Myocardium--Imaging; Deep learning (Machine learning)

Publication Date

4-5-2023

Document Type

Dissertation

Student Type

Graduate

Degree Name

Imaging Science (Ph.D.)

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)

Advisor

Cristian A. Linte

Advisor/Committee Member

Carl Salvaggio

Advisor/Committee Member

Suzanne M. Shontz

Campus

RIT – Main Campus

Plan Codes

IMGS-PHD

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