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Abstract #3671

Improving classifications of brain tumor tissue with Sparse Dictionary Learning based analysis of dynamic susceptibility contrast enhanced MRI data

Silun Wang1, Shu Zhang2, Liya Wang3, Bing Ji4, Tianming Liu5, and Hui Mao1

1Emory University School of Medicine, Atlanta, GA, United States, 2The University of Georgia, Athens, China, 3Long Hua Hospital, Shenzhen, China, 4Emory University School of Medicine, Atlanta, China, 5The University of Georgia, Athens, GA, United States

We analyzed the DSC MRI signals based on patterns of descriptive DSE-MR parameters by using Sparse Dictionary Learning (SDL) coding method. We successfully decomposed DSC MRI signals into linear combinations of multiple components based on sparse representation of DSC MRI signals in the tumor region of tumor core and peritumoral edema which might be represent multiple heterogeneity component in brain tumors. Assessment of diagnostic performance of SVM classification after cross validation revealed that the combination of conventional DSC temporal characteristics and dictionary learning based DSC temporal features would result in the best classification accuracy between tumor core and peritumoral edema (with total diagnostic accuracy of 77%, AUC 0.78).

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