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A computerized image analysis framework for dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) with applications to breast cancer
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Agner, Shannon Christine. A computerized image analysis framework for dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) with applications to breast cancer. Retrieved from https://doi.org/doi:10.7282/T3KK9B4K

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TitleA computerized image analysis framework for dynamic
contrast enhanced magnetic resonance imaging
(DCE-MRI) with applications to breast cancer
NameAgner, Shannon Christine (author); Madabhushi, Anant (chair); Ganesan, Shridar (internal member); Cai, Li (internal member); Rosen, Mark A (outside member); Feldman, Michael D (outside member); Rutgers University; Graduate School - New Brunswick
Date Created2011
Other Date2011-05 (degree)
SubjectBiomedical Engineering, Breast--Magnetic resonance imaging, Breast--Cancer--Magnetic resonance imaging
Extentxix, 107 p. : ill.
DescriptionDynamic contrast enhanced magnetic resonance imaging (DCE-MRI) provides a wealth of information about the anatomy of the breast, particularly in the setting of breast cancer diagnosis. In addition to the images it provides regarding the architecture of
breast tissue, it also provides functional information about blood flow by means of the DCE study. The sensitivity of DCE-MRI has been reported at close to 100%, so the difficult tasks for the radiologist in reviewing breast DCE-MRI are: (1) discerning
between which lesions are benign and which are malignant; and (2) doing so for a patient study that involves hundreds of images and is 4-dimensional. Because of the great detail and volume of information DCE-MRI provides, computational methods for both extracting and analyzing information derived from the images are useful in distilling the entire patient study down to the most salient images and features for the
radiologist to examine. In this dissertation, computer-based methods developed for
analyzing the data acquired in a breast DCE-MRI patient study are described. In the first part, pre-processing methods used for aligning the images of the timedependent DCE study are explained. Because segmentation is important for describing the morphology of the lesion as well as the region of interest for any subsequent quantitative analysis of a lesion, as a second step to pre-processing, a spectral embedding based active contour (SEAC) method for segmentation of lesions is developed and tested. A
feature developed for extracting the spatiotemporal characteristics of breast lesions, termed textural kinetics, is then described, and its utility is demonstrated for distinguishing benign from malignant lesions as well as in identifying triple negative breast
lesions, a lesion type that is extremely aggressive and has no targeted therapies. Finally, these quantitative methods are summarized in a computer aided diagnosis framework that provides insight into the biologic nature of breast lesion subtypes as well as for
directing treatment and determining prognosis.
NotePh.D.
NoteIncludes bibliographical references
Noteby Shannon Christine Agner
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/T3KK9B4K
Languageeng
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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