Image reconstruction of compressed sensing MRI using graph-based redundant wavelet transform

Lai, Zongying; Qu, Xiaobo; Liu, Yunsong; Guo, Di; Ye, Jing; Zhan, Zhifang; Chen, Zhong

doi:10.1016/j.media.2015.05.012

Cited by 151 publications

(85 citation statements)

References 37 publications

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“…One main advantage of pFISTA is that reconstructed errors are stable to the step size, thus allowing widely usage for different tight frames in magnetic resonance image reconstructions. In te future, the convergence of pFISTA for general frames/dictionaries will be analyzeed and this algorithm will be used for other advanced adaptively sparse representations [52][53] in compressed sensing MRI.…”

Section: Discussionmentioning

confidence: 99%

Projected Iterative Soft-Thresholding Algorithm for Tight Frames in Compressed Sensing Magnetic Resonance Imaging

Liu

Zhan

Cai

et al. 2016

IEEE Trans. Med. Imaging

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156

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Abstract-Compressed sensing has shown great potentials in accelerating magnetic resonance imaging. Fast image reconstruction and high image quality are two main issues faced by this new technology. It has been shown that, redundant image representations, e.g. tight frames, can significantly improve the image quality. But how to efficiently solve the reconstruction problem with these redundant representation systems is still challenging. This paper attempts to address the problem of applying iterative soft-thresholding algorithm (ISTA) to tight frames based magnetic resonance image reconstruction. By introducing the canonical dual frame to construct the orthogonal projection operator on the range of the analysis sparsity operator, we propose a projected iterative soft-thresholding algorithm (pISTA) and further accelerate it by incorporating the strategy proposed by Beck and Teboulle in 2009. We theoretically prove that pISTA converges to the minimum of a function with a balanced tight frame sparsity. Experimental results demonstrate that the proposed algorithm achieves better reconstruction than the widely used synthesis sparse model and the accelerated pISTA converges faster or comparable to the state-of-art smoothing FISTA. One major advantage of pISTA is that only one extra parameter, the step size, is introduced and the numerical solution is stable to it in terms of image reconstruction errors, thus allowing easily setting in many fast magnetic resonance imaging applications.

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Section: Discussionmentioning

confidence: 99%

Projected Iterative Soft-Thresholding Algorithm for Tight Frames in Compressed Sensing Magnetic Resonance Imaging

Liu

Zhan

Cai

et al. 2016

IEEE Trans. Med. Imaging

Self Cite

156

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“…Sparse representation can be explored in a specific transform domain or generally in a dictionary-based subspace [16]. Classic fast CS-MRI uses predefined and fixed sparsifying transforms, e.g., total variation (TV) [17], [18], [19], discrete cosine transforms [20], [21], [22] and discrete wavelet transforms [23], [24], [25]. In addition, this has been extended to a more flexible sparse representation learnt directly from data using dictionary learning [26], [27], [28].…”

Section: Related Work and Our Contributions A Classic Model-basementioning

confidence: 99%

DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction

Yang

Dong

et al. 2018

IEEE Trans. Med. Imaging

886

628

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Abstract-Compressed Sensing Magnetic Resonance Imaging (CS-MRI) enables fast acquisition, which is highly desirable for numerous clinical applications. This can not only reduce the scanning cost and ease patient burden, but also potentially reduce motion artefacts and the effect of contrast washout, thus yielding better image quality. Different from parallel imaging based fast MRI, which utilises multiple coils to simultaneously receive MR signals, CS-MRI breaks the Nyquist-Shannon sampling barrier to reconstruct MRI images with much less required raw data. This paper provides a deep learning based strategy for reconstruction of CS-MRI, and bridges a substantial gap between conventional non-learning methods working only on data from a single image, and prior knowledge from large training datasets. In particular, a novel conditional Generative Adversarial Networks-based model (DAGAN) is proposed to reconstruct CS-MRI. In our DAGAN architecture, we have designed a refinement learning method to stabilise our U-Net based generator, which provides an endto-end network to reduce aliasing artefacts. To better preserve texture and edges in the reconstruction, we have coupled the adversarial loss with an innovative content loss. In addition, we incorporate frequency domain information to enforce similarity in both the image and frequency domains. We have performed comprehensive comparison studies with both conventional CS-MRI reconstruction methods and newly investigated deep learning approaches. Compared to these methods, our DAGAN method provides superior reconstruction with preserved perceptual image details. Furthermore, each image is reconstructed in about 5 ms, which is suitable for real-time processing.

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“…Experiment results demonstrate that the image quality of the low-resolution image can be truly improved if the contrast-invariant weight is borrowed from the high resolution image of another contrast. In the future, we plan to further improve the sharpness of edges and textures by utilizing sparse representation [26–29] and local geometric directions [30–32]. The code of this work is available at http://www.quxiaobo.org/project/MultiContrastMRI/Toolbox_MultiContrastMRI_Superresolution.zip.…”

Section: Discussionmentioning

confidence: 99%

Multi-contrast brain magnetic resonance image super-resolution using the local weight similarity

Zheng

Bai

et al. 2017

BMC Med Imaging

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BackgroundLow-resolution images may be acquired in magnetic resonance imaging (MRI) due to limited data acquisition time or other physical constraints, and their resolutions can be improved with super-resolution methods. Since MRI can offer images of an object with different contrasts, e.g., T1-weighted or T2-weighted, the shared information between inter-contrast images can be used to benefit super-resolution.MethodsIn this study, an MRI image super-resolution approach to enhance in-plane resolution is proposed by exploring the statistical information estimated from another contrast MRI image that shares similar anatomical structures. We assume some edge structures are shown both in T1-weighted and T2-weighted MRI brain images acquired of the same subject, and the proposed approach aims to recover such kind of structures to generate a high-resolution image from its low-resolution counterpart.ResultsThe statistical information produces a local weight of image that are found to be nearly invariant to the image contrast and thus this weight can be used to transfer the shared information from one contrast to another. We analyze this property with comprehensive mathematics as well as numerical experiments.ConclusionExperimental results demonstrate that the image quality of low-resolution images can be remarkably improved with the proposed method if this weight is borrowed from a high resolution image with another contrast.Graphical Abstract Multi-contrast MRI Image Super-resolution with Contrast-invariant Regression WeightsElectronic supplementary materialThe online version of this article (doi:10.1186/s12880-016-0176-2) contains supplementary material, which is available to authorized users.

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Image reconstruction of compressed sensing MRI using graph-based redundant wavelet transform

Cited by 151 publications

References 37 publications

Projected Iterative Soft-Thresholding Algorithm for Tight Frames in Compressed Sensing Magnetic Resonance Imaging

Projected Iterative Soft-Thresholding Algorithm for Tight Frames in Compressed Sensing Magnetic Resonance Imaging

DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction

Multi-contrast brain magnetic resonance image super-resolution using the local weight similarity

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