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- W2309554013 abstract "Purpose k-t principle component analysis (k-t PCA) is a distinguished method for high spatiotemporal resolution dynamic MRI. To further improve the accuracy of k-t PCA, a combination with partial parallel imaging (PPI), k-t PCA/SENSE, has been tested. However, k-t PCA/SENSE suffers from long reconstruction time and limited improvement. This study aims to improve the combination of k-t PCA and PPI on both reconstruction speed and accuracy. Methods A sequential combination scheme called k-t PCA GROWL (GRAPPA operator for wider readout line) was proposed. The GRAPPA operator was performed before k-t PCA to extend each readout line into a wider band, which improved the condition of the encoding matrix in the following k-t PCA reconstruction. k-t PCA GROWL was tested and compared with k-t PCA and k-t PCA/SENSE on cardiac imaging. Results k-t PCA GROWL consistently resulted in better image quality compared with k-t PCA/SENSE at high acceleration factors for both retrospectively and prospectively undersampled cardiac imaging, with a much lower computation cost. The improvement in image quality became greater with the increase of acceleration factor. Conclusion By sequentially combining the GRAPPA operator and k-t PCA, the proposed k-t PCA GROWL method outperformed k-t PCA/SENSE in both reconstruction speed and accuracy, suggesting that k-t PCA GROWL is a better combination scheme than k-t PCA/SENSE. Magn Reson Med 77:1058–1067, 2017. © 2016 International Society for Magnetic Resonance in Medicine" @default.
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- W2309554013 date "2016-03-26" @default.
- W2309554013 modified "2023-10-11" @default.
- W2309554013 title "Sequential combination of k-t principle component analysis (PCA) and partial parallel imaging: k-t PCA GROWL" @default.
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- W2309554013 doi "https://doi.org/10.1002/mrm.26187" @default.
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