IEICE TRANSACTIONS on Information
Compressed Sensing in Magnetic Resonance Imaging Using Non-Randomly Under-Sampled Signal in Cartesian Coordinates
Summary :
Image quality depends on the randomness of the k-space signal under-sampling in compressed sensing MRI (CS-MRI), especially for two-dimensional image acquisition. We investigate the feasibility of non-random signal under-sampling CS-MRI to stabilize the quality of reconstructed images and avoid arbitrariness in sampling point selection. Regular signal under-sampling for the phase-encoding direction is adopted, in which sampling points are chosen at equal intervals for the phase-encoding direction while varying the sampling density. Curvelet transform was adopted to remove the aliasing artifacts due to regular signal under-sampling. To increase the incoherence between the measurement matrix and the sparsifying transform function, the scale of the curvelet transform was varied in each iterative image reconstruction step. We evaluated the obtained images by the peak-signal-to-noise ratio and root mean squared error in localized 3×3 pixel regions. Simulation studies and experiments showed that the signal-to-noise ratio and the structural similarity index of reconstructed images were comparable to standard random under-sampling CS. This study demonstrated the feasibility of non-random under-sampling based CS by using the multi-scale curvelet transform as a sparsifying transform function. The technique may help to stabilize the obtained image quality in CS-MRI.
- Publication
- IEICE TRANSACTIONS on Information Vol.E102-D No.9 pp.1851-1859
- Publication Date
- 2019/09/01
- Publicized
- 2019/05/31
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2019EDP7016
- Type of Manuscript
- PAPER
- Category
- Biological Engineering
Authors
Ryo KAZAMA
Graduate School of Engineering, Utsunomiya University
Kazuki SEKINE
Nisshin Information System Development
Satoshi ITO
Graduate School of Engineering, Utsunomiya University
Keyword
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Ryo KAZAMA, Kazuki SEKINE, Satoshi ITO, "Compressed Sensing in Magnetic Resonance Imaging Using Non-Randomly Under-Sampled Signal in Cartesian Coordinates" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 9, pp. 1851-1859, September 2019, doi: 10.1587/transinf.2019EDP7016.
Abstract: Image quality depends on the randomness of the k-space signal under-sampling in compressed sensing MRI (CS-MRI), especially for two-dimensional image acquisition. We investigate the feasibility of non-random signal under-sampling CS-MRI to stabilize the quality of reconstructed images and avoid arbitrariness in sampling point selection. Regular signal under-sampling for the phase-encoding direction is adopted, in which sampling points are chosen at equal intervals for the phase-encoding direction while varying the sampling density. Curvelet transform was adopted to remove the aliasing artifacts due to regular signal under-sampling. To increase the incoherence between the measurement matrix and the sparsifying transform function, the scale of the curvelet transform was varied in each iterative image reconstruction step. We evaluated the obtained images by the peak-signal-to-noise ratio and root mean squared error in localized 3×3 pixel regions. Simulation studies and experiments showed that the signal-to-noise ratio and the structural similarity index of reconstructed images were comparable to standard random under-sampling CS. This study demonstrated the feasibility of non-random under-sampling based CS by using the multi-scale curvelet transform as a sparsifying transform function. The technique may help to stabilize the obtained image quality in CS-MRI.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDP7016/_p
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@ARTICLE{e102-d_9_1851,
author={Ryo KAZAMA, Kazuki SEKINE, Satoshi ITO, },
journal={IEICE TRANSACTIONS on Information},
title={Compressed Sensing in Magnetic Resonance Imaging Using Non-Randomly Under-Sampled Signal in Cartesian Coordinates},
year={2019},
volume={E102-D},
number={9},
pages={1851-1859},
abstract={Image quality depends on the randomness of the k-space signal under-sampling in compressed sensing MRI (CS-MRI), especially for two-dimensional image acquisition. We investigate the feasibility of non-random signal under-sampling CS-MRI to stabilize the quality of reconstructed images and avoid arbitrariness in sampling point selection. Regular signal under-sampling for the phase-encoding direction is adopted, in which sampling points are chosen at equal intervals for the phase-encoding direction while varying the sampling density. Curvelet transform was adopted to remove the aliasing artifacts due to regular signal under-sampling. To increase the incoherence between the measurement matrix and the sparsifying transform function, the scale of the curvelet transform was varied in each iterative image reconstruction step. We evaluated the obtained images by the peak-signal-to-noise ratio and root mean squared error in localized 3×3 pixel regions. Simulation studies and experiments showed that the signal-to-noise ratio and the structural similarity index of reconstructed images were comparable to standard random under-sampling CS. This study demonstrated the feasibility of non-random under-sampling based CS by using the multi-scale curvelet transform as a sparsifying transform function. The technique may help to stabilize the obtained image quality in CS-MRI.},
keywords={},
doi={10.1587/transinf.2019EDP7016},
ISSN={1745-1361},
month={September},}
Copy
TY - JOUR
TI - Compressed Sensing in Magnetic Resonance Imaging Using Non-Randomly Under-Sampled Signal in Cartesian Coordinates
T2 - IEICE TRANSACTIONS on Information
SP - 1851
EP - 1859
AU - Ryo KAZAMA
AU - Kazuki SEKINE
AU - Satoshi ITO
PY - 2019
DO - 10.1587/transinf.2019EDP7016
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2019
AB - Image quality depends on the randomness of the k-space signal under-sampling in compressed sensing MRI (CS-MRI), especially for two-dimensional image acquisition. We investigate the feasibility of non-random signal under-sampling CS-MRI to stabilize the quality of reconstructed images and avoid arbitrariness in sampling point selection. Regular signal under-sampling for the phase-encoding direction is adopted, in which sampling points are chosen at equal intervals for the phase-encoding direction while varying the sampling density. Curvelet transform was adopted to remove the aliasing artifacts due to regular signal under-sampling. To increase the incoherence between the measurement matrix and the sparsifying transform function, the scale of the curvelet transform was varied in each iterative image reconstruction step. We evaluated the obtained images by the peak-signal-to-noise ratio and root mean squared error in localized 3×3 pixel regions. Simulation studies and experiments showed that the signal-to-noise ratio and the structural similarity index of reconstructed images were comparable to standard random under-sampling CS. This study demonstrated the feasibility of non-random under-sampling based CS by using the multi-scale curvelet transform as a sparsifying transform function. The technique may help to stabilize the obtained image quality in CS-MRI.
ER -
English
