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Quantitative integration of imaging and non-imaging data
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Tiwari, Pallavi. Quantitative integration of imaging and non-imaging data. Retrieved from https://doi.org/doi:10.7282/T3XP73GJ

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TitleQuantitative integration of imaging and non-imaging data
NameTiwari, Pallavi (author); Madabhushi, Anant (chair); Boustany, Nada (internal member); Ganeshan, Sridhar (internal member); Feldman, Michael (outside member); Bloch, Nicholas (outside member); Rutgers University; Graduate School - New Brunswick
Date Created2012
Other Date2012-05 (degree)
SubjectBiomedical Engineering, Data integration (Computer science), Prostate--Diseases--Diagnosis, Prostate--Cancer--Magnetic resonance imaging
Extentxxiii, 118 p. : ill.
DescriptionThe problem of data integration involving imaging and non-imaging modalities is largely unexplored in the biomedical eld, mainly due to the challenges in quantitatively combining such heterogeneous modalities existing in diff erent dimensions and scales. Although several methods have been proposed in the literature involving quantitative integration of multi-protocol imaging, there has been a paucity of similar biomedical tools for quantitative integration of imaging and non-imaging data. In this work, we present novel data integration schemes to overcome the aforementioned challenges limiting the integration of imaging and non-imaging modalities, and hence improve disease characterization. Our novel data integration methods are applied to integration of multi-parametric Magnetic Resonance (MR) imaging (MP-MRI)-structural MR imaging with metabolic spectroscopic information (non-imaging) for improved prostate cancer (CaP) diagnosis, grading, and treatment evaluation post-radiation therapy (RT). To this end, we have developed novel data integration schemes such as, Multimodal Wavelet Embedding Representation for data Combination (MaWERiC), and Semi-Supervised Multi-Kernel (SeSMiK) Graph Embedding, which fi rst uniformly represent individual data modalities into a common framework using dimensionality reduction and kernel embedding techniques, followed by a seamless integration of imaging and non-imaging data in the common framework. The integrated quantitative signatures thus obtained are shown to be signifi cantly more diagnostically informative as compared to any single modality. Similar improvement in results was observed using integrated MP-MRI signatures for evaluating radiation therapy related changes in CaP patients, with an aim to identify (a) pre-RT disease extent along with extra capsule spread (if any) and (b) residual disease on post-RT MP-MRI.
NotePh.D.
NoteIncludes bibliographical references
NoteIncludes vita
Noteby Pallavi Tiwari
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/T3XP73GJ
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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