Rosette Trajectories for Fast MRI Based on an Adaptive Reconstruction Method

Wen Xu,Qiaoyu Tian,Jin Xu,Xiaomei Yang,Kai Liu

doi:10.1109/access.2021.3062020

Wen Xu, Qiaoyu Tian + Show 3 more

Open Access

https://doi.org/10.1109/access.2021.3062020

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Abstract

Non-Cartesian MRI k-space trajectories provide faster and more motion-robust data acquisitions than those of Cartesian trajectories. In this paper, we focus on the rosette trajectory and generalize the weighted rosette trajectories for fast undersampled k-space data acquisition. However, single-slice imaging using the rosette trajectory will be affected by the off-resonance effect. To reduce the artifacts from off-resonance slices, this paper introduced an adaptive iterative reconstruction method derived from the TV and nuclear norm regularization terms for raw MRI data reconstruction. This reconstruction problem was separated into two subproblems and solved with an adaptive regularization parameter. The results show that the weighted rosette trajectory that offers short acquisition times and good off-resonance behaviors with little blurring can be used for reconstruction with adaptive regularization parameters and achieve a superior performance.

Highlights

Magnetic resonance imaging (MRI) is widely used in medical diagnosis; the procedure of obtaining magnetic resonance imaging requires patients to hold their breath for up to several seconds, creating an uncomfortable situation for many patients and introducing artifacts and blurring
The weighted rosette sampling trajectory proposed in this paper achieves an improved signal-to-noise ratio (SNR) value under various algorithms, and the chest, brain, and heart images show an obvious performance from the Table 1
Limited by the inability of using real MRI equipment, we could not calculate the sampling time, but comparisons with time of reconstruction algorithm yielded results that showed the time of different algorithms differ great, for instance FIRLS_TV algorithm preserve the fast convergence performance with 2.5s, compared to our adaptive algorithm with 19.81s, but our algorithm provides more precise results especially in the amplitude and phase of complex MR image

Summary

Introduction

Magnetic resonance imaging (MRI) is widely used in medical diagnosis; the procedure of obtaining magnetic resonance imaging requires patients to hold their breath for up to several seconds, creating an uncomfortable situation for many patients and introducing artifacts and blurring. Reducing the time of data acquisition and improving the image quality has been a core objective of MRI development. The theory of compressed sensing imaging has provided a new way to shorten the time of data acquisition [1], [2]. Lustig et al applied compressed sensing for rapid MR images [3] and introduced a randomly perturbed

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Results

Conclusion