Inherently self-calibrating non-cartesian parallel imaging

Non-Cartesian Parallel Magnetic Resonance Imaging

Regularized Least Squares Estimating Sensitivity for Self-calibrating Parallel Imaging

Calibration of the spatial sensitivity functions of coil arrays is a crucial element in parallel magnetic resonance imaging (pMRI). The self-calibrating technique for sensitivity extraction has complemented the common calibration technique that uses a separate pre-scan. In order to improve the accuracy of sensitivity estimate from small number of self-calibrating data, which is extracted from a...

Parallel Imaging Reconstruction I: Cartesian

Introduction Parallel Imaging is an effective way of accelerating MRI acquisitions. It works by replacing some of the Fourier encoding with spatial sensitivity encoding from multiple localized receiver coils. Fewer k-space locations are visited during the acquisition and to compensate for the missing data, information about the spatial sensitivities of the receiver coils is incorporated into th...

Parallel Imaging Reconstruction I: Cartesian

Parallel Imaging is an effective way of accelerating MRI acquisitions. It works by replacing some of the Fourier encoding with spatial sensitivity encoding from multiple localized receiver coils. Fewer k-space locations are visited during the acquisition and to compensate for the missing data, information about the spatial sensitivities of the receiver coils is incorporated into the reconstruct...

Rapid 3D Parallel Imaging of Non-Cartesian Data

Introduction: The 3D Pretty Easy Parallel Imaging (PEPI) technique, previously presented as a 2D technique [1], is a coil sensitivity based method used for reconstructing arbitrary 3D k-space trajectories. PEPI is an iterative technique that enforces receive b1-field and sampled data consistency in each iteration (eg. [2-4]). The 3D trajectory used is Spiral Projection Imaging (SPI) [3]. Method...