Direct & accelerated parameter mapping using the unscented Kalman filter
Li Zhao 1 and Craig H. Meyer 1,2
Biomedical Engineering, University of
Virginia, Charlottesville, Virginia, United States,
University of Virginia, Charlottesville, Virginia,
Parameter mapping is essential for clinic diagnose and
its acceleration is highly demanded. With under sampling
in kspace-parameter encoding space, we proposed an
unscented Kalman filter based method to estimation the
parameter directly without reconstruction of the
interval images. This method was verified in accelerated
T2 mapping on numerical phantom and volunteer data.
Comparing to compressed sensing with K-SVD, unscented
Kalman filter provides more accurate T2 map in less
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