Improved l1-SPIRiT using 3D walsh transform-based sparsity basis

Zhao, Feng; F, Liu; Jiang, Mingfeng; Crozier, Stuart; Guo, He; Wang, Yuxin

doi:10.1016/j.mri.2014.04.008

Cited by 11 publications

(15 citation statements)

References 32 publications

(32 reference statements)

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“…For example, the discrete wavelet transform [11], curvelet transform [12], Walsh transform [13], and singular value decomposition-based transforms [14], [15] are commonly used as sparsity transforms. However, without sufficient k-space data, sparse coefficients transformed from the sampling signals cannot faithfully represent the original object, and thus, the quality of the reconstructed images is degraded.…”

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confidence: 99%

Compressed Sensing MRI via Two-stage Reconstruction

Yang

et al. 2015

IEEE Trans. Biomed. Eng.

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Compressed sensing (CS) has been applied to magnetic resonance imaging for the acceleration of data collection. However, existing CS techniques usually produce images with residual artifacts, particularly at high reduction factors. In this paper, we propose a novel, two-stage reconstruction scheme, which takes advantage of the properties of k-space data and under-sampling patterns that are useful in CS. In this algorithm, the under-sampled k-space data is segmented into low-frequency and high-frequency domains. Then, in stage one, using dense measurements, the low-frequency region of k-space data is faithfully reconstructed. The fully reconstituted low-frequency k-space data from the first stage is then combined with the high-frequency k-space data to complete the second stage reconstruction of the whole of k-space. With this two-stage approach, each reconstruction inherently incorporates a lower data under-sampling rate and more homogeneous signal magnitudes than conventional approaches. Because the restricted isometric property is easier to satisfy, the reconstruction consequently produces lower residual errors at each step. Compared with a conventional CS reconstruction, for the cases of cardiac cine, brain and angiogram imaging, the proposed method achieves a more accurate reconstruction with an improvement of 2-4 dB in peak signal-to-noise ratio respectively, using reduction factors of up to 6.

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confidence: 99%

Compressed Sensing MRI via Two-stage Reconstruction

Yang

et al. 2015

IEEE Trans. Biomed. Eng.

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“…From previous case studies, the results manifested the effects of weighting adjustment in different k-space parts. We also note that, the 2D wavelet sparsity in the two-stage reconstruction can be replaced by other proper sparsifying transformations [80,109].…”

Section: Discussionmentioning

confidence: 99%

“…Other sparsity bases can also be applied to the proposed method, such as wavelet transform, Walsh transform [109] and tensor decomposition based transform [80], for specific imaging studies. The developed algorithm can be easily combined with parallel MR imaging, for more rapid imaging studies.…”

Section: Extension Of the Proposed Methodsmentioning

confidence: 99%

“…For example, the discrete wavelet transform [8], curvelet transform [98], walsh transform [109] and singular value decomposition-based transforms [80,99] are commonly used as sparsity transform. However, without sufficient k-space data, sparse coefficients transformed from the sampling signals cannot faithfully represent the original object and thus the quality of the reconstructed images is degraded.…”

Section: Introductionmentioning

confidence: 99%

“…The third category refers to optimization methods for nonlinear signal recovery [7,8,109] These have been a focus of research interest since CS was introduced. These algorithms, such as the focal underdetermined system solution, the iterative re-weighted least squares and the conjugate gradients, enforce both the image sparsity and data consistency between the reconstruction and the acquisition.…”

Section: Introductionmentioning

confidence: 99%

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Improving the image quality in compressed sensing MRI by the exploitation of data properties

Yang¹

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Minimizing scan time without compromising image quality has been the main thrust of magnetic resonance imaging (MRI) since the establishment of the field. Tremendous effort has been put into MRI systems in pursuit of faster MR imaging techniques, which can substantially facilitate clinical diagnoses and improve the patient experience. Traditional MRI accelerated approaches mainly focused on hardware improvements to improve the speed of scanning. These methods are still governed by Nyquist-Shannon sampling rate. Hence, when the acceleration rate is pushed beyond the theoretical limit, aliasing artefacts are introduced which degrade the reconstructed image quality and are difficult to eliminate via current methods. Recently, it has been found that a newly developed signal processing theory, compressed sensing (CS), can be used to create high-resolution signal data sets from sparse samples, despite violating the classical Nyquist-Shannon sampling criteria. The application of CS to MRI opens up an appealing avenue to offer potentially significant scan time reductions. CS reconstructs MR images from incomplete k-space measurements. Taking advantage of the fact that MR images have sparse representations under certain mathematical transformations, CS overcomes the distortions resulting from the under-sampled k-space data. Significant progress has been made in the development of CS MRI. However, there are two major remaining challenges to its full adoption in clinical applications: (1) the small amount of under-sampled data (for example, less than 1/8 of the whole k-space data) will inherently introduce artefacts in the reconstructed image and compromise diagnosis accuracy for the reconstructed images; (2) owing to hardware limits, it is difficult to realise two-dimension random under-sampling, which is favoured by CS operation.Practically, the random phase-encode under-sampling pattern has been widely implemented.However, it introduces coherent aliasing artefacts that are hard to eliminate using the CS theory.In this thesis, several techniques are proposed that can improve the quality of images reconstructed by CS MRI. These techniques have the potential to facilitate faithful reconstruction of CS MRI in clinical applications. A novel two-stage reconstruction scheme is introduced in Chapter 3. In this method, the under-sampled k-space data is segmented into low-frequency and high-frequency parts.Then, in stage one, using dense measurements, the low-frequency region of k-space data is faithfully reconstructed. The fully reconstituted low-frequency k-space data from the first stage is then combined with the under-sampled high-frequency k-space data to complete the second stage reconstruction of the whole image. With this two-stage approach, each reconstruction inherently incorporates a lower data under-sampling rate than conventional approaches. Because the restricted isometric property is easier to satisfy than conventional CS method, the reconstruction consequently ii produces lower residual errors in each step...

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Bi-smooth constraints for accelerated dynamic MRI with low-rank plus sparse tensor decomposition

Liu

Luan

2023

Biomedical Signal Processing and Control

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Improved l1-SPIRiT using 3D walsh transform-based sparsity basis

Cited by 11 publications

References 32 publications

Compressed Sensing MRI via Two-stage Reconstruction

Compressed Sensing MRI via Two-stage Reconstruction

Improving the image quality in compressed sensing MRI by the exploitation of data properties

Bi-smooth constraints for accelerated dynamic MRI with low-rank plus sparse tensor decomposition

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