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Abstract #2298

A Noise Suppression Approach in the Quantitative Analysis of DCE Images

Renjie He 1 , Yao Ding 2 , Clifton Fuller 2 , Qi Liu 1 , and Weiguo Zhang 3

1 United Imaging Healthcare America, Houston, Texas, United States, 2 MDACC, Texas, United States, 3 United Imaging Healthcare, Shanghai, China

Instead of averaging over multiple (repetitive) acquisitions to reduce the parameter map uncertainty caused by noise in the head and neck region, firstly we introduce a non-local means spatial filtering to reduce the noise from a single acquisition. The noise is further suppressed by incorporating model-based filtering originated from the sparse coding theory where a joint-dictionary is applied. The joint-dictionary also provide an approach to extrapolate the flip angles from the collected 6 flip angles data set to the regenerated 28 virtual flip angles. Finally, we construct another model-based full dictionary to retrieve the T1 from the reconstruction of 28 flip angles, and S0 is acquired by least square estimation from the T1 map.

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