HF-SENSE: an improved partially parallel imaging using a high-pass filter
2019
Background One of the major limitations of MRI is its slow acquisition speed. To accelerate
data acquisition, partially parallel imaging (PPI) methods have been widely used in clinical applications such as sensitivity encoding (SENSE) and generalized autocalibrating partially parallel acquisitions (GRAPPA). SENSE is a popular image-domain partially parallel imaging method, which suffers from residual
aliasingartifacts when the reduction factor goes higher.
Undersamplingthe
k-spacedata and then reconstruct images with artificial sparsity is an efficient way to accelerate
data acquisition. By exploiting artificial sparsity with a
high-pass filter, an improved SENSE method is proposed in this work, termed
high-pass filteredSENSE (HF-SENSE).
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