title: DISTORTION-OPTIMAL SELF-CALIBRATING PARALLEL MRI BY BLIND INTERPOLATION IN SUBSAMPLED FILTER BANKS.
abstract: Self-calibrating k-space-based image reconstruction in parallel MRI interpolates the subsampled multi-channel data to a fully sampled Nyquist grid in k-space. Adopting a filter bank interpolation framework, we provide a new formulation of the associated inverse problem and develop the theory for blind identification of the interpolant filters. The developed method is applied to imaging scenarios where high effective acceleration is desired and is shown to be capable of reconstructing artifact-free images with minimal amount of calibration data - hence, achieving high effective accelerations. Simulation and in-vivo results indicate that improved image quality, and thus greater scan time reductions compared to the state-of-the-art method of GRAPPA can be achieved.
authors.affiliation: Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA ; Coordinated Science Laboratory, ECE Department, University of Illinois, Urbana-Champaign, IL, USA.
authors.forename: Behzad
authors.initials: B
authors.lastname: Sharif
authors.affiliation: Coordinated Science Laboratory, ECE Department, University of Illinois, Urbana-Champaign, IL, USA.
authors.forename: Yoram
authors.initials: Y
authors.lastname: Bresler
keywords: Blind Identification; Image Reconstruction; MIMO; Multi-channel Interpolation; Parallel MRI; Self-calibrating