Space Filling Curves for MRI Sampling

Space Filling Curves for MRI Sampling

Sharma, S. (Indian Institute of Science)
Hari, K.V.S. (Indian Institute of Science)
Leus, G.J.T. (TU Delft Signal Processing Systems)

2020

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.

MRI
k-space trajectories
space filling curves

http://resolver.tudelft.nl/uuid:9eabdf99-580c-4358-a0b6-786e04f4f27e

https://doi.org/10.1109/ICASSP40776.2020.9054372

IEEE

2020-11-14

978-1-5090-6632-2

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): Proceedings

ICASSP 2020, 2020-05-04 → 2020-05-08, Barcelona, Spain

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2020 S. Sharma, K.V.S. Hari, G.J.T. Leus