Meeting Banner
Abstract #2788

Learning multichannel coil combination with Automated Transform by Manifold Approximation (AUTOMAP) using complex-valued neural networks

Bo Zhu1,2, Stephen Cauley1, Bruce R. Rosen1, and Matthew S Rosen1,2

1A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States, 2Department of Physics, Harvard University, Cambridge, MA, United States

End-to-end learning of the image reconstruction domain transform with AUTOMAP (Automated Transform by Manifold Approximation) has been demonstrated on a variety of spatial encoding strategies previously limited to single-channel data. We extend this framework to learning reconstruction of highly undersampled multichannel k-space data solely from pairs of multichannel k-space and image training data without employing conventional parallel imaging formulations such as SENSE or GRAPPA, and show improved RMSE and artifact reduction with the trained AUTOMAP reconstruction network.

This abstract and the presentation materials are available to members only; a login is required.

Join Here