Principles and Progress in ultrafast 2D spatiotemporally encoded MRI

Abstract

Magnetic resonance imaging (MRI) is an indispensable tool used in both the lab and the clinic. Part of the strength of MRI comes from its ability to deliver anatomical information highlighted with different types of contrasts, including functional and diffusion-oriented acquisitions that are often incompatible with normal, multi-shot scans. For these problems, Nobel-award-winning techniques such as Echo Planar Imaging (EPI) have been essential in opening a manifold of new applications. EPI, however, has challenges when dealing with sharp changes in magnetic susceptibility, including those arising in the presence of air/tissue or air/fat interfaces, from non-ferromagnetic metal implants, as well when the main magnetic field cannot be shimmed to achieve the desired degree of homogeneity, as often is the case in systems built using permanent magnets. Among the techniques being proposed to deal with this kind of problem is spatiotemporally-encoded (SPEN) MRI. The present review focuses on the principles of this technique, with an emphasis on: i) explaining SPEN's resilience to field inhomogeneities, on the basis of expanded bandwidth considerations vis-à-vis EPI; ii) “the good, the bad and the ugly” associated with the undersampling that SPEN usually has to carry out when employing expanded bandwidths; iii) recent developments in data processing algorithms seeking to alleviate the “bad and the ugly” part of these experiments by formulating SPEN image reconstruction as an optimization problem, and then relying on compressed sensing and parallel imaging concepts to achieve improved image quality; and iv) the incorporation of experimental improvements including scan interleaving, simultaneous multi-banding and multi-echo elements, to keep in line with advancements in other areas of fast MRI. The strengths and weaknesses of these data sampling and processing strategies are assessed, and examples of their leverage in functional, but foremost diffusion-weighted, imaging applications, are presented.