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Abstract #4470

Evaluation of Diffusion Spectrum Imaging Reconstruction with Trained Dictionaries use of 3T MR

Ping-Hong Yeh 1 , Namgyun Lee 2 , John Morissette 3 , Arman A. Taheri 3 , Li-Wei Kuo 4 , Fang-Cheng Yeh 5 , Erick Jorge Canales- Rodrguez 6 , Wei Lui 3 , John Ollinger 3 , Terrence Oakes 3 , Mark L. Ettenhofer 7 , and Gerard Riedy 3

1 Henry Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, United States, 2 Korea Basic Science Institute, Korea, 3 National Capital Neuroimaging Consortium, Bethesda, MD, United States, 4 National Health Research Institutes, Taiwan, 5 Carnegie Mellon University, PA, United States, 6 FIDMAG Research Foundation, Germanes Hospitalaries and CIBERSAM, Barcelona, Spain, 7 7Uniformed Services University of the Health Sciences, MD, United States

Recent work using Compressed Sensing (CS) reconstruction shows promising in greatly reducing diffusion spectrum imaging (DSI) scan time without jeopardizing critical image information. We evaluate the performance of CS reconstruction using dictionary-based training coupled with the Focal Underdetermined System Solver (FOCUSS) algorithm and L2 regularization on undersampled human brain DSI data acquired by a clinical 3T MR scanner within an acceptable time frame (< 20 minutes).

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