Automatic high-bandwidth calibration and reconstruction of arbitrarily sampled parallel MRI.

Automatic high-bandwidth calibration and reconstruction of arbitrarily sampled parallel MRI.

Blog Article

Today, many MRI reconstruction techniques exist for undersampled MRI data.Regularization-based techniques inspired by compressed sensing allow for the reconstruction of undersampled data that would lead to an ill-posed reconstruction problem.Parallel imaging enables the reconstruction of MRI images from undersampled multi-coil data Towel Bar that leads to a well-posed reconstruction problem.Autocalibrating pMRI techniques encompass pMRI techniques where no explicit knowledge of the coil sensivities is required.A first purpose of this paper is to derive a novel autocalibration approach for pMRI that allows for the estimation and use of smooth, but high-bandwidth coil profiles instead of a compactly supported kernel.

These high-bandwidth models adhere more accurately to the physics of an antenna system.The second purpose of this paper is to demonstrate the feasibility of a parameter-free reconstruction algorithm that combines autocalibrating pMRI and Food compressed sensing.Therefore, we present several techniques for automatic parameter estimation in MRI reconstruction.Experiments show that a higher reconstruction accuracy can be had using high-bandwidth coil models and that the automatic parameter choices yield an acceptable result.