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Deformable models and machine learning for large-scale cardiac MRI image analytics
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Yang, Dong. Deformable models and machine learning for large-scale cardiac MRI image analytics. Retrieved from https://doi.org/doi:10.7282/t3-x9v0-9704

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TitleDeformable models and machine learning for large-scale cardiac MRI image analytics
NameYang, Dong (author); Metaxas, Dimitris N. (chair); Michmizos, Konstantinos (internal member); Zhang, Yongfeng (internal member); Duncan, James (outside member); Rutgers University; School of Graduate Studies
Date Created2019
Other Date2019-05 (degree)
SubjectComputer Science, Heart -- Magnetic resonance imaging, Machine learning
Extent1 online resource (xvi, 97 pages) : illustrations
DescriptionThe analysis of left ventricle (LV) wall motion is an important step for understanding cardiac functioning mechanisms, and clinical diagnosis of ventricular diseases. For example, ventricular dyssynchrony is one of the major causes for heart failure; treatment of dyssynchrony, e.g. Cardiac Resynchronization Therapy (CRT), can help some patients preventing failure. Conventional diagnosis methods, including electrocardiogram (ECG) and ultrasound imaging, provide only coarse characterization of dyssynchrony patterns, such as global function indices or qualitative assessment of motion patterns. To achieve a more comprehensive understanding of ventricular dyssynchrony, we propose a novel approach to study the regional patterns of left ventricle (LV) wall using cardiac magnetic resonance imaging (MRI). Firstly, we extract the myocardial contours from long- and short-axis cine MRI, and compensate for respiration offsets through rigid transformation to reconstruct the 3D shell of the heart wall. Then an unsupervised learning method using deep neural networks is adopted to compute the in-plane deformation field. Next, the 3D volumetric LV wall motion and deformation fields are recovered by using deformable models and spatial interpolation. Finally, in order to characterize the regional motion of the LV wall, a conventional 17-segment model is utilized for dividing the reconstructed 3D model, so that the local dyssynchrony patterns can be well-determined. Our proposed approach has a great potential to be applied in the analysis of large-scale MRI datasets of various cardiovascular diseases, and used to guide the administration of CRT. Moreover, we include other applications for further demonstration of our approaches.
NotePh.D.
NoteIncludes bibliographical references
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/t3-x9v0-9704
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.
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