The purpose of this study is to eliminate the aliasing artifacts in accerelated radial MRI. We designed a Cross-Domain deep-learning network, called SISI-Net(Sinogram-Image-Sinogram-Image Network). This is an architecture to gradually solves data sparsity problems by iteratively learning the radial sampling data in the sinogram domain and the reconstructed data in the image domain. As a result, proposed network could remove aliasing artifacts effectively while maintaining structural information.
This abstract and the presentation materials are available to members only; a login is required.