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Cho, Hyungjoon
Biomedical Imaging Science and Engineering Lab(BISE)
Research Interests
  • Imaging tumor microenvironments, susceptibility contrast based MR structural imaging, ultra fast acquisitions of dynamic MR, algorithm/Hardware development for magnetic particle imaging

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Gaussian mixture model-based classification of dynamic contrast enhanced MRI data for identifying diverse tumor microenvironments: preliminary results

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Title
Gaussian mixture model-based classification of dynamic contrast enhanced MRI data for identifying diverse tumor microenvironments: preliminary results
Author
Han, S. H.Ackerstaff, E.Stoyanova, R.Carlin, S.Huang, W.Koutcher, J. A.Kim, J. K.Cho, G.Jang, Gil-JinCho, Hyungjoon
Keywords
DCE-MRI; Dynamic contrast enhanced; Dynamic contrast enhanced MRI; Gaussian Mixture Model; Hypoxia; Model-based classifications; Positron emission tomography (PET); Tumor microenvironments
Issue Date
201305
Publisher
WILEY-BLACKWELL
Citation
NMR IN BIOMEDICINE, v.26, no.5, pp.519 - 532
Abstract
Tumor hypoxia develops heterogeneously, affects radiation sensitivity and the development of metastases. Prognostic information derived from the in vivo characterization of the spatial distribution of hypoxic areas in solid tumors can be of value for radiation therapy planning and for monitoring the early treatment response. Tumor hypoxia is caused by an imbalance between the supply and consumption of oxygen. The tumor oxygen supply is inherently linked to its vasculature and perfusion which can be evaluated by dynamic contrast enhanced (DCE-) MRI using the contrast agent Gd-DTPA. Thus, we hypothesize that DCE-MRI data may provide surrogate information regarding tumor hypoxia. In this study, DCE-MRI data from a rat prostate tumor model were analysed with a Gaussian mixture model (GMM)-based classification to identify perfused, hypoxic and necrotic areas for a total of ten tumor slices from six rats, of which one slice was used as training data for GMM classifications. The results of pattern recognition analyzes were validated by comparison to corresponding Akep maps defining the perfused area (0.84 +/- 0.09 overlap), hematoxylin and eosin (H&E)-stained tissue sections defining necrosis (0.64 +/- 0.15 overlap) and pimonidazole-stained sections defining hypoxia (0.72 +/- 0.17 overlap), respectively. Our preliminary data indicate the feasibility of a GMM-based classification to identify tumor hypoxia, necrosis and perfusion/permeability from non-invasively acquired, in vivo DCE-MRI data alone, possibly obviating the need for invasive procedures, such as biopsies, or exposure to radioactivity, such as positron emission tomography (PET) exams.
URI
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DOI
http://dx.doi.org/10.1002/nbm.2888
ISSN
0952-3480
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ECE_Journal Papers
SLS_Journal Papers

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