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Abstract #0348

Fast MR Parameter Mapping from Highly Undersampled Data by Direct Reconstruction of Principal Component Coefficient Maps Using Compressed Sensing

Chuan Huang1, Christian Graff2, Ali Bilgin3, Maria I. Altbach4

1Mathematics, University of Arizona, Tucson, AZ, United States; 2Program in Applied Mathematics, University of Arizona; 3Biomedical Engineering, University of Arizona; 4Radiology, University of Arizona

There has been an increased interest in quantitative MR parameter mapping techniques which enable direct comparison of tissue-related values between different subjects and scans. However the lengthy acquisition times needed by conventional parameter mapping methods limit their use in the clinic. In this work, we introduce a new model-based approach to reconstruct accurate T2 maps directly from highly undersampled FSE data. The proposed approach referred to as DIrect REconstruction of Principal COmponent coefficient Maps (DIREPCOM) removes non-linearity from the model and employs sparsity constraints in both the spatial and temporal dimensions to produces accurate T2 maps by using Principal Component Analysis. While this proposed technique has been illustrated for T2 estimation, the methodology can be adapted to the estimation of other MR parameters.