Towards a general framework for fast and feasible k-space trajectories for MRI based on projection methods

Shubham Sharma, Mario Coutino, Sundeep Prabhakar Chepuri, Geert Leus, K. V.S. Hari

Research output: Contribution to journalArticleScientificpeer-review


The design of feasible trajectories to traverse the k-space for sampling in magnetic resonance imaging (MRI) is important while considering ways to reduce the scan time. Over the recent years, non-Cartesian trajectories have been observed to result in benign artifacts and being less sensitive to motion. In this paper, we propose a generalized framework that encompasses projection-based methods to generate feasible non-Cartesian k-space trajectories. This framework allows to construct feasible trajectories from both random or structured initial trajectories, e.g., based on the traveling salesman problem (TSP). We evaluate the performance of the proposed methods by simulating the reconstruction of 128 × 128 and 256 × 256 phantom and brain MRI images in terms of structural similarity (SSIM) index and peak signal-to-noise ratio (PSNR) using compressed sensing techniques. It is observed that the TSP-based trajectories from the proposed projection method with constant acceleration parameterization (CAP) result in better reconstruction compared to the projection method with constant velocity parameterization (CVP) and this for a similar read-out time. Further, random-like trajectories are observed to be better than TSP-based trajectories as they reduce the read-out time while providing better reconstruction quality. A reduction in read-out time by upto 67% is achieved using the proposed projection with permutation (PP) method.

Original languageEnglish
Pages (from-to)122-134
Number of pages13
JournalMagnetic Resonance Imaging
Publication statusPublished - Oct 2020


  • compressed sensing
  • k-space sampling
  • MRI
  • projection methods
  • trajectory design

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