O-space with high resolution readouts outperforms radial imaging

Abstract

Purpose While O-Space imaging is well known to accelerate image acquisition beyond traditional Cartesian sampling, its advantages compared to undersampled radial imaging, the linear trajectory most akin to O-Space imaging, have not been detailed. In addition, previous studies have focused on ultrafast imaging with very high acceleration factors and relatively low resolution. The purpose of this work is to directly compare O-Space and radial imaging in their potential to deliver highly undersampled images of high resolution and minimal artifacts, as needed for diagnostic applications. We report that the greatest advantages to O-Space imaging are observed with extended data acquisition readouts. Theory and Methods A sampling strategy that uses high resolution readouts is presented and applied to compare the potential of radial and O-Space sequences to generate high resolution images at high undersampling factors. Simulations and phantom studies were performed to investigate whether use of extended readout windows in O-Space imaging would increase k-space sampling and improve image quality, compared to radial imaging. Results Experimental O-Space images acquired with high resolution readouts show fewer artifacts and greater sharpness than radial imaging with equivalent scan parameters. Radial images taken with longer readouts show stronger undersampling artifacts, which can cause small or subtle image features to disappear. These features are preserved in a comparable O-Space image. Conclusions High resolution O-Space imaging yields highly undersampled images of high resolution and minimal artifacts. The additional nonlinear gradient field improves image quality beyond conventional radial imaging.

Spread‐spectrum magnetic resonance imaging

“…The use of local, nonlinear magnetic modulation fields spreads this corkscrew trajectory into a dense band of k‐space sampling points, as depicted in Figure . These trajectories show a certain similarity to the trajectories generated by the O‐space or FRONSAC method resulting in a space variant resolution. However, in spread‐spectrum MRI each single coil can be driven independently, leading to a much higher flexibility and degrees of freedom in possible modulation patterns as compared to only quadratically varying fields used in O‐space or FRONSAC.…”

“…In conventional imaging based on linear gradient field encoding, single points in k‐space are sampled along a Cartesian trajectory or grid. Using nonlinear magnetic fields for encoding spreads each single point in k‐space to a pattern or stamp of certain shape and size, similar to the O‐space or FRONSAC methods that are based on spatially quadratically varying magnetic fields . The k‐space pattern sampled along a single readout is shown in Figure right bottom for two combinations of modulation strengths and frequencies.…”

“…For example, UNFOLD, k‐tBLAST/SENSE, or compressed sensing and its derivatives use sparse sampling in k‐space and time, combined with prior knowledge to boost imaging speed further in certain applications . More recently, the use of nonlinear gradients for spatial encoding such as PatLoc, FRONSAC, and O‐space imaging has been proposed with the potential to provide tailored spatially varying resolution, curved imaging slices that mirror physiological geometry, and faster parallel imaging capabilities with multichannel coils . Both linear and nonlinear magnetic fields can be combined and used for image encoding, which offers significantly more degrees of freedom to encode the MR signal.…”

Adaptive Volterra Filter for Parallel MRI Reconstruction

“…It contains second-order terms to suppress noise with the non-linear structure of the reconstruction system. The future research will focus on how to solve the problem of reconstruction quality through using a smaller number of ACS lines and attempt to combine the proposed method with the previous nonlinear gradient applications [23,24]. Since the second-order terms can enlarge errors generated by outliers in the low-frequency region, how to find a way to avoid the estimation error to reduce aliasing artifacts is also a major goal.…”

Positive‐contrast susceptibility imaging based on first‐order primal‐dual optimization

“…Experimental evidence has demonstrated that the proposed PD algorithm can achieve robust and accurate positive‐contrast images in a reasonable time. In the future, the proposed method would be highly relevant for positive‐contrast susceptibility imaging in the clinic as well as other preclinical applications of nonlinear gradients …”