Space Filling Curves for MRI Sampling

Shubham Sharma, K.V.S. Hari, Geert Leus

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)

Abstract

A novel class of k-space trajectories for magnetic resonance imaging (MRI) sampling using space filling curves (SFCs) is presented here. More specifically, Peano, Hilbert and Sierpinski curves are used. We propose 1-shot and 4-shot variable density SFCs by utilizing the space coverage provided by SFCs in different iterations. The proposed trajectories are compared with state-of-the-art echo planar imaging (EPI) trajectories for 128 × 128 and 256 × 256 phantom and brain images. The simulation results show that the readout time is reduced by up to 45% for the 128 × 128 image with little compromise in reconstruction quality. Also, the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) index are improved by 2.32 dB and 0.1009, respectively, with an 18% shorter readout time using the 4-shot Hilbert SFC trajectory for reconstructing a 256 × 256 brain MRI image.
Original languageEnglish
Title of host publicationICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Subtitle of host publicationProceedings
PublisherIEEE
Pages1115-1119
Number of pages5
ISBN (Electronic)978-1-5090-6631-5
ISBN (Print)978-1-5090-6632-2
DOIs
Publication statusPublished - 2020
EventICASSP 2020: IEEE International Conference on Acoustics, Speech and Signal Processing - Barcelona, Spain
Duration: 4 May 20208 May 2020

Conference

ConferenceICASSP 2020
CountrySpain
CityBarcelona
Period4/05/208/05/20

Keywords

  • MRI
  • k-space trajectories
  • space filling curves

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