Home
Resource
Integrated reconstruction, segmentation and functional analysis for MRI data
PDF
PDF format is widely accepted and good for printing.
Plug-in required
PDF-1(57.21 MB)
Citation & Export
View Usage Statistics
Staff View

Citation & Export
Hide

Simple citation

Huang, Qiaoying. Integrated reconstruction, segmentation and functional analysis for MRI data. Retrieved from https://doi.org/doi:10.7282/t3-13qy-p528

Export

Click here for information about Citation Management Tools at Rutgers.

Statistics
Hide

Description

TitleIntegrated reconstruction, segmentation and functional analysis for MRI data
NameHuang, Qiaoying (author); Metaxas, Dimitris (chair); Zhang, Desheng (internal member); Michmizos, Konstantinos (internal member); Qian, Zhen (outside member); Rutgers University; School of Graduate Studies
Date Created2021
Other Date2021-05 (degree)
SubjectComputer Science, MRI reconstruction, Magnetic resonance imaging
Extent1 online resource (xv, 109 pages) : illustrations
DescriptionMagnetic Resonance Imaging (MRI) plays an important role in many clinical applications due to its low radiation and high-contrast imaging. Traditional sequential pipeline for analyzing MRI data includes three steps: reconstruction, segmentation, and functional analysis. For dynamic MRI, i.e., dynamic cardiac MRI, the motion estimation step is also included. These tasks are highly entangled and share important information. However, current approaches are either computationally inefficient or require lots of human efforts to design complicated neural architectures. Moreover, limited work has explored the relationship between different tasks and utilized the coherence information between them. In this thesis, we propose 1) more effective and efficient deep learning-based MRI reconstruction methods; 2) integrated approaches for joint reconstruction and segmentation as well as joint reconstruction and motion estimation; 3) thickness and thickening estimation methods of the left ventricle wall for the diagnosis and characterization of different cardiac diseases. The proposed methods are extensively validated on four different medical applications: 2D cardiac and brain MRI reconstruction, 2D cardiac MRI reconstruction and segmentation, dynamic 3D cardiac MRI reconstruction and motion estimation, dynamic 3D cardiac MRI thickness and thickening estimation. Our proposed approaches are evaluated on various datasets and have outperformed current state-of-the-art methods in all these studies.
NotePh.D.
NoteIncludes bibliographical references
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/t3-13qy-p528
LanguageEnglish
CollectionSchool of Graduate Studies Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
Version 8.5.5
Rutgers University Libraries - Copyright ©2024