MRI reconstruction from partial k-space data by iterative stationary wavelet transform thresholding
نویسندگان
چکیده
One approach to accelerating data acquisition in magnetic resonance imaging is acquisition of partial k-space data and recovery of missing data based on the sparsity of the image in the wavelet transform domain. We hypothesize that the application of stationary wavelet transform (SWT) thresholding as a sparsity-promoting operation results in improved artifact removal performance in comparison with the regular decimated wavelet transform (DWT). On this grounds, we develop an iterative SWT thresholding reconstruction. We demonstrate that an acceleration factor of 3 can be achieved using this approach on a human brain scan while only suffering a 9.61% penalty in signal to noise ratio without compromising the spatial resolution of the image.
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