Data convolution and combination operation (COCOA) for motion ghost artifacts reduction

A novel method, data convolution and combination operation, is introduced for the reduction of ghost artifacts due to motion or flow during data acquisition. Since neighboring k‐space data points from different coil elements have strong correlations, a new “synthetic” k‐space with dispersed motion artifacts can be generated through convolution for each coil. The corresponding convolution kernel can be self‐calibrated using the acquired k‐space data. The synthetic and the acquired data sets can be checked for consistency to identify k‐space areas that are motion corrupted. Subsequently, these two data sets can be combined appropriately to produce a k‐space data set showing a reduced level of motion induced error. If the acquired k‐space contains isolated error, the error can be completely eliminated through data convolution and combination operation. If the acquired k‐space data contain widespread errors, the application of the convolution also significantly reduces the overall error. Results with simulated and in vivo data demonstrate that this self‐calibrated method robustly reduces ghost artifacts due to swallowing, breathing, or blood flow, with a minimum impact on the image signal‐to‐noise ratio. Magn Reson Med, 2010. © 2010 Wiley‐Liss, Inc.