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High‐pass GRAPPA: An image support reduction technique for improved partially parallel imaging
Author(s) -
Huang Feng,
Li Yu,
Vijayakumar Sathya,
Hertel Sarah,
Duensing George R.
Publication year - 2008
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.21495
Subject(s) - computer science , reduction (mathematics) , artifact (error) , kernel (algebra) , artificial intelligence , noise reduction , filter (signal processing) , noise (video) , computer vision , image (mathematics) , image denoising , pattern recognition (psychology) , mathematics , geometry , combinatorics
Partially parallel imaging (PPI) is a widely used technique in clinical applications. A limitation of this technique is the strong noise and artifact in the reconstructed images when high reduction factors are used. This work aims to increase the clinical applicability of PPI by improving its performance at high reduction factors. A new concept, image support reduction, is introduced. A systematic filter‐design approach for image support reduction is proposed. This approach shows more advantages when used with an important existing PPI technique, GRAPPA. An improved GRAPPA method, high‐pass GRAPPA (hp‐GRAPPA), was developed based on this approach. The new technique does not involve changing the original GRAPPA kernel and performs reconstruction in almost the same amount of time. Experimentally, it is demonstrated that the reconstructed images using hp‐GRAPPA have much lower noise/artifact level than those reconstructed using GRAPPA. Magn Reson Med, 2008. © 2008 Wiley‐Liss, Inc.