The Influence of Radial Undersampling Schemes on Compressed Sensing in Cardiac DTI.

Jianping Huang,Yuemin Zhu,Wenlong Song,Lihui Wang

doi:10.3390/s18072388

Abstract

Diffusion tensor imaging (DTI) is known to suffer from long acquisition time, which greatly limits its practical and clinical use. Undersampling of k-space data provides an effective way to reduce the amount of data to acquire while maintaining image quality. Radial undersampling is one of the most popular non-Cartesian k-space sampling schemes, since it has relatively lower sensitivity to motion than Cartesian trajectories, and artifacts from linear reconstruction are more noise-like. Therefore, radial imaging is a promising strategy of undersampling to accelerate acquisitions. The purpose of this study is to investigate various radial sampling schemes as well as reconstructions using compressed sensing (CS). In particular, we propose two randomly perturbed radial undersampling schemes: golden-angle and random angle. The proposed methods are compared with existing radial undersampling methods, including uniformity-angle, randomly perturbed uniformity-angle, golden-angle, and random angle. The results on both simulated and real human cardiac diffusion weighted (DW) images show that, for the same amount of k-space data, randomly sampling around a random radial line results in better reconstruction quality for DTI indices, such as fractional anisotropy (FA), mean diffusivities (MD), and that the randomly perturbed golden-angle undersampling yields the best results for cardiac CS-DTI image reconstruction.

Highlights

To date, almost all clinical magnetic resonance imaging (MRI) is performed by acquiring k-space data along a Cartesian trajectory, which means that data are sampled line-by-line on a rectangular grid.k-space can be sampled in an arbitrary non-Cartesian manner, and different sampling trajectories will have different properties and implications for the reconstructed image [1]
Maps reconstructed from the sampled k-space data with non-perturbed sampling patterns, and (e)–(g) are fractional anisotropy (FA) maps reconstructed from the sampled k-space data with perturbed sampling patterns
We observe that the randomly perturbed sampling methods proposed in this work were clearly superior to the non-perturbed ones, especially for the simulation dataset (Figure 4) and the second acquisition dataset (Figure 6), but the visual assessment were not evident

Summary

Introduction

Almost all clinical magnetic resonance imaging (MRI) is performed by acquiring k-space data along a Cartesian trajectory, which means that data are sampled line-by-line on a rectangular grid. K-space can be sampled in an arbitrary non-Cartesian manner, and different sampling trajectories will have different properties and implications for the reconstructed image [1]. Radial sampling is one of the most frequently used non-Cartesian k-space sampling schemes, firstly proposed by Lauterbur in 1973 [2], which samples k-space along spokes instead of grid lines. In traditionally uniform radial sampling scheme, k-space is sampled with spaced radial lines; each line is restricted to a constant length of the acquisition window and requires a new scan for each desired temporal resolution.

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