公开(公告)号：US11231475B2

公开(公告)日：2022-01-25

申请号：US16853308

申请日：2020-04-20

申请人： REGENTS OF THE UNIVERSITY OF MINNESOTA

发明人： Steen Moeller ,  Mehmet Akcakaya ,  Seng-Wei Chieh

IPC分类号： G01R33/56 ,  G01R33/561 ,  G01R33/565 ,  G01R33/58 ,  G01R33/48

摘要： A fully sampled calibration data set, which may be Cartesian k-space data, is used to obtain targeted and optimal interpolation kernels for non-regularly sampled data. The calibration data are self-calibration data obtained from a time-averaged image, or re-sampled data. ACS data are resampled for calibration of region-specific kernels. Subsequently, an explicit noise-based regularized solution can be utilized to estimate region-specific kernels for reconstruction.

公开(公告)号：US20200333416A1

公开(公告)日：2020-10-22

申请号：US16853308

申请日：2020-04-20

申请人： REGENTS OF THE UNIVERSITY OF MINNESOTA

发明人： Steen Moeller ,  Mehmet Akcakaya ,  Seng-Wei Chieh

IPC分类号： G01R33/56 ,  G01R33/561 ,  G01R33/48 ,  G01R33/58 ,  G01R33/565

摘要： A fully sampled calibration data set, which may be Cartesian k-space data, is used to obtain targeted and optimal interpolation kernels for non-regularly sampled data. The calibration data are self-calibration data obtained from a time-averaged image, or re-sampled data. ACS data are resampled for calibration of region-specific kernels. Subsequently, an explicit noise-based regularized solution can be utilized to estimate region-specific kernels for reconstruction.