Eric Pierre1,2, Nicole Seiberlich3, Vikas Gulani4, Pierrick Bourgeat2, Olivier Salvado2, Mark Griswold3
1Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States; 2ICT Centre, CSIRO, Brisbane, QLD, Australia; 3Radiology, Case Western Reserve University, Cleveland, OH, United States; 4Radiology, Case Western Reserve University, Cleveland, United States
In order to improve the GRAPPA reconstruction of an undersampled object at high reduction factors, the previously introduced ABSINTHE technique required a large training set of MR signal with matching coil configuration. This study seeks to further increase the effectiveness and applicability of ABSINTHE by allowing the addition of any MR image to the training set regardless of its coil configuration. Furthermore, it introduces key image preprocessing steps which noticeably increase the relevance of each image in the training set. An improved image quality is shown in simulated and in vivo data compared to GRAPPA and the previous ABSINTHE technique.