Parallel Imaging Reconstruction by Sense Algorithm

The problem of image reconstruction from sensitivity encoded data is formulated in a general fashion and solved for arbitrary coil configurations and k-space sampling patterns. To achieve increased acquisition speed in magnetic resonance imaging Special attention is given to the currently most practical case, namely, sampling a common Cartesian grid with reduced density. Scan time was reduced to one-half using a four-coil array in brain imaging

A Maximum Likelihood Approach to Parallel Imaging With Coil Sensitivity Noise

Parallel imaging is a powerful technique to speed up Magnetic Resonance (MR) image acquisition via multiple coils. Both the received signal of each coil and its sensitivity map, which describes its spatial response, are needed during reconstruction. Widely used schemes such as SENSE assume that sensitivity maps of the coils are noiseless while the only errors are due to a noisy signal. In practice, however sensitivity maps are subject to a wide variety of errors. At first glance, sensitivity noise appears to result in an errors-in-variables problem of the kind that is typically solved using Total Least Squares (TLS). However, existing TLS algorithms are inappropriate for the specific type of block structure that arises in parallel imaging. In this paper we take a maximum likelihood approach to the problem of parallel imaging in the presence of independent Gaussian sensitivity noise. This results in a quasi-quadratic objective function, which can be efficiently minimized. Experimental evidence suggests substantial gains over conventional SENSE, especially in non-ideal imaging conditions like low SNR, high g-factors, large acceleration and misaligned sensitivity maps