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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