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 64:157-166, 2010. (C) 2010 Wiley-Liss, Inc.