Dynamic magnetic resonance imaging method based on golden-ratio cartesian sampling and compressed sensing

Li, Shuo; Zhu, Yanchun; Xie, Yaoqin; Gao, Song

doi:10.1371/journal.pone.0191569

Cited by 7 publications

(8 citation statements)

References 16 publications

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“…The compressed sensing (CS) technique was introduced to overcome these disadvantages. 8,9 The CS technique consists of random undersampling and image reconstruction that is tuned for sparse data, 10 where "sparse data" means that there are relatively few significant pixels with nonzero values. For example, angiograms are extremely sparse in pixel representation.…”

Section: D Mri Can Bementioning

confidence: 99%

Hybrid of Compressed Sensing and Parallel Imaging Applied to Three-dimensional Isotropic T<sub>2</sub>-weighted Turbo Spin-echo MR Imaging of the Lumbar Spine

Morita

Nakaura

Maruyama³

et al. 2020

MRMS

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Purpose:The hybrid compressed sensing (hybrid-CS) technique can shorten the acquisition time compared with the sensitivity encoding (SENSE) technique in lumbar MRI. To evaluate the feasibility of a hybrid-CS technique in comparison with 3D isotropic T 2 -weighted turbo spin-echo (3D volume isotropic turbo spinecho acquisition [VISTA]) MRI of the lumbar spine. Materials and Methods:The Institutional Review Board approved this study and informed consent was obtained from participants prior to study entry. Sixteen healthy volunteers underwent lumbar spine 3D VISTA with conventional parallel imaging for SENSE and hybrid-CS at 3T. We recorded the image acquisition times of SENSE and hybrid-CS. We compared the signal-to-noise ratio (SNR) in spine, cerebrospinal fluid (CSF), lumbar disc, epidural fat, and erector spinae muscle, and the contrast of spine, CSF, and disc, and performed qualitative image analysis assessment, between the two image sequences. Results:The image acquisition time for hybrid-CS was 39.2% shorter than that of SENSE (218.4/358.8 s). The contrast of CSF and SNR of the spine was significantly higher with hybrid-CS than with SENSE (P < 0.05). The SNR of the disc and muscle was significantly higher with SENSE than with hybrid-CS (P < 0.05). There were no significant differences in the contrast of spine, disc, and fat, and SNR of CSF and fat between hybrid-CS and SENSE. There were no significant differences in the qualitative evaluation between hybrid-CS and SENSE. Conclusion:Compared with SENSE, hybrid-CS for 3D VISTA can shorten image acquisition time without sacrificing image quality.

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Section: D Mri Can Bementioning

confidence: 99%

Hybrid of Compressed Sensing and Parallel Imaging Applied to Three-dimensional Isotropic T<sub>2</sub>-weighted Turbo Spin-echo MR Imaging of the Lumbar Spine

Morita

Nakaura

Maruyama³

et al. 2020

MRMS

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“…Distinguishing features of CAVA include: (i) temporal resolution that can be adjusted retrospectively, (ii) variable density to preferentially sample central k‐space at a higher rate, (iii) maintaining incoherence, and (iv) ensuring a fully sampled time‐averaged k‐space to facilitate sensitivity map estimation. This work differs from that by Li et al in addressing features (ii) and (iv). The sampling pattern here is governed by adjustable parameters controlling local sample density, in contrast to the inflexible pattern in Li et al, which devotes over 10% of samples to one central k‐space location.…”

Section: Introductionmentioning

confidence: 58%

“…The sampling pattern here is governed by adjustable parameters controlling local sample density, in contrast to the inflexible pattern in Li et al, which devotes over 10% of samples to one central k‐space location. The results here also evaluate imaging performance for prospective sampling from ten healthy volunteers, in contrast to simulated data from a digital phantom considered in Li et al…”

Section: Introductionmentioning

confidence: 91%

CArtesian sampling with Variable density and Adjustable temporal resolution (CAVA)

Rich

Gregg

Jin

et al. 2019

Magnetic Resonance in Med

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Purpose To develop a variable density Cartesian sampling method that allows retrospective adjustment of temporal resolution for dynamic MRI applications and to validate it in real‐time phase contrast MRI (PC‐MRI). Theory and Methods The proposed method, called CArtesian sampling with Variable density and Adjustable temporal resolution (CAVA), begins by producing a sequence of phase encoding indices based on the golden ratio increment. Then, variable density is introduced by nonlinear stretching of the indices. Finally, the elements of the resulting sequence are rounded up to the nearest integer. The performance of CAVA is evaluated using PC‐MRI data from a pulsatile flow phantom and real‐time, free‐breathing data from ten healthy volunteers. Results CAVA enabled image recovery at various temporal resolutions that were selected retrospectively. For the pulsatile flow phantom, image quality and flow quantification accuracy from CAVA were comparable to that from another pseudo‐random sampling pattern with fixed temporal resolution. In addition, flow quantification results based on CAVA were in good agreement with a breath‐held segmented acquisition. Conclusions By allowing retrospective binning of the MRI data, CAVA provides an avenue to retrospectively adjust the temporal resolution of PC‐MRI.

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“…The SILVER framework also has a potential to be extended to imaging with other trajectories that have previously been derived from the golden ratio, e.g. spirals (30,31), cones (32), and even Cartesian sampling (33).…”

Section: Discussionmentioning

confidence: 99%

The Set Increment with Limited Views Encoding Ratio (SILVER) Method for Optimizing Radial Sampling of Dynamic MRI

Schauman

Okell

Chiew

2020

Preprint

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PurposeTo present and assess a method for choosing the increment between spokes in radially sampled MRI that can produce higher SNR than golden ratio derived methods.Theory and MethodsSampling uniformity determines image SNR when reconstructed using linear methods. Thus, for a radial trajectory, uniformly spaced sampling is ideal. However, uniform sampling lacks post-acquisition reconstruction flexibility, which is often needed in dynamic imaging. Golden ratio-based methods are often used for this purpose. The method presented here, Set Increment with Limited Views Encoding Ratio (SILVER), optimizes sampling uniformity when the number of spokes per frame is approximately known a-priori. With SILVER, an optimization algorithm finds the angular increment that provides the highest uniformity for a pre-defined set of reconstruction window sizes. The optimization cost function was based on an electrostatic model of uniformity. SILVER was tested over multiple sets and assessed in terms of uniformity, analytical g-factor, and SNR both in simulation and applied to dynamic arterial spin labeling angiograms in three healthy volunteers.ResultsAll SILVER optimizations produced higher or equal uniformity than the golden ratio within the predefined sets. The SILVER method converged to the golden ratio for broad optimization sets. As hypothesized, the g-factors for SILVER were higher than for uniform sampling, but, on average, 26% lower than golden ratio. Image SNR followed the same trend both in simulation and in vivo.ConclusionSILVER is a simple addition to any sequence currently using golden ratio sampling and it has a small but measurable effect on sampling efficiency.

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Dynamic magnetic resonance imaging method based on golden-ratio cartesian sampling and compressed sensing

Cited by 7 publications

References 16 publications

Hybrid of Compressed Sensing and Parallel Imaging Applied to Three-dimensional Isotropic T<sub>2</sub>-weighted Turbo Spin-echo MR Imaging of the Lumbar Spine

Hybrid of Compressed Sensing and Parallel Imaging Applied to Three-dimensional Isotropic T<sub>2</sub>-weighted Turbo Spin-echo MR Imaging of the Lumbar Spine

CArtesian sampling with Variable density and Adjustable temporal resolution (CAVA)

The Set Increment with Limited Views Encoding Ratio (SILVER) Method for Optimizing Radial Sampling of Dynamic MRI

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