title: Sequential combination of k-t principle component analysis (PCA) and partial parallel imaging: k-t PCA GROWL.
abstract: 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.
  
  
  
  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.
  
  
  
  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.
  
  
  
  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.
authors.affiliation: Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.
authors.forename: Haikun
authors.initials: H
authors.lastname: Qi
authors.affiliation: Philips Healthcare, Suzhou, China.
authors.forename: Feng
authors.initials: F
authors.lastname: Huang
authors.affiliation: Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
authors.forename: Hongmei
authors.initials: H
authors.lastname: Zhou
authors.affiliation: Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.
authors.forename: Huijun
authors.initials: H
authors.lastname: Chen
mesh_terms: D000328:Adult; D000465:Algorithms; D057791:Cardiac Imaging Techniques / Q000379:methods*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D018810:Magnetic Resonance Angiography / Q000379:methods*; D019028:Magnetic Resonance Imaging, Cine / Q000379:methods*; D008297:Male; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted
keywords: GROWL; cardiac imaging; k-t PCA; partial parallel imaging