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Data Consistency Criterion for Selecting Parameters for K-space-based Reconstruction in Parallel Imaging by Roger Nana; Xiaoping Hu is a Medicine article available to read on EtoBox.

What is Data Consistency Criterion for Selecting Parameters for K-space-based Reconstruction in Parallel Imaging about?

k-space-based reconstruction in parallel imaging depends on the reconstruction kernel setting, including its support. An optimal choice of the kernel depends on the calibration data, coil geometry and signal-to-noise ratio, as well as the criterion used. In this work, data consistency, imposed by the shift invariance requirement of the kernel, is introduced as a goodness measure of k-space-based reconstruction in parallel imaging and demonstrated. Data consistency error (DCE) is calculated as the sum of squared difference between the acquired signals and their estimates obtained based on the interpolation of the estimated missing data. A resemblance between DCE and the mean square error in the reconstructed image was found, demonstrating DCE's potential as a metric for comparing or choosing reconstructions. When used for selecting the kernel support for generalized autocalibrating partially parallel acquisition (GRAPPA) reconstruction and the set of frames for calibration as well as the kernel support in temporal GRAPPA reconstruction, DCE led to improved images over existing methods. Data consistency error is efficient to evaluate, robust for selecting reconstruction parameters an

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Author

Roger Nana; Xiaoping Hu

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0730-725X)

Published

2010

Language

EN

Field

Medicine (Health Sciences)