Learning-based method for k-space trajectory design in MRI

Conference paper (2022)

Authors

Shubham Sharma Indian Institute of Science
K. V.S. Hari Indian Institute of Science
G.J.T. Leus Signal Processing Systems - EEMCS
Research Group
Signal Processing Systems (EEMCS) (TU Delft)
Copyright: © 2022 Shubham Sharma, K.V.S. Hari, G.J.T. Leus
DOI: https://doi.org/10.1109/EMBC48229.2022.9871692
MRI Data-driven Variable-density samplin K-space trajectory design
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Published Date

2022

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Signal Processing Systems

Abstract

Variable density sampling of the k-space in MRI is an integral part of trajectory design. It has been observed that data-driven trajectory design methods provide a better image reconstruction as compared to trajectories obtained from a fixed or a parametric density function. In this paper, a data-driven strategy has been proposed to obtain non-Cartesian continuous k-space sampling trajectories for MRI under the compressed sensing framework (greedy non-Cartesian (GNC)). A stochas-tic version of the algorithm (stochastic greedy non-Cartesian (SGNC)) is also proposed that reduces the computation time. We compare the proposed trajectory with a traveling salesman problem (TSP)-based trajectory and an echo planar imaging-like trajectory obtained by a greedy method called stochastic greedy-Cartesian (SGC) algorithm. The training images are taken from knee images of the fastMRI dataset. It is observed that the proposed algorithms outperform the TSP-based and the SGC trajectories for similar read-out times.