Vectorial total generalized variation for accelerated multi-channel multi-contrast MRI

Chatnuntawech, Itthi; Martín, Adrián; Bilgic̦, Berkin; Setsompop, Kawin; Adalsteinsson, Elfar; Schiavi, Emanuele

doi:10.1016/j.mri.2016.05.014

Cited by 23 publications

(20 citation statements)

References 32 publications

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“…It has been successfully employed as a regularizer in grey-valued image processing in [4,5,6,22]. Recently, the T GV 2 (u) has been extended to vector-valued images for regularization in joint reconstruction of PET and MR images [23] and multi-channel/contrast MR images [10].…”

Section: Related Workmentioning

confidence: 99%

A Two-Stage Algorithm for Joint Multimodal Image Reconstruction

Chen¹,

Li²,

Ye³

2019

SIAM J. Imaging Sci.

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We propose a new two-stage joint image reconstruction method by recovering edge directly from observed data and then assembling image using the recovered edges. More specifically, we reformulate joint image reconstruction with vectorial total-variation regularization as an l 1 minimization problem of the Jacobian of the underlying multi-modality or multi-contrast images. We provide detailed derivation of data fidelity for Jacobian in Radon and Fourier transform domains. The new minimization problem yields an optimal convergence rate higher than that of existing primal-dual based reconstruction algorithms, and the per-iteration cost remains low by using closed-form matrix-valued shrinkages. We conducted numerical tests on a number of multi-contrast CT and MR image datasets, which demonstrate that the proposed method significantly improves reconstruction efficiency and accuracy compared to the state-ofthe-arts joint image reconstruction methods.

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Section: Related Workmentioning

confidence: 99%

A Two-Stage Algorithm for Joint Multimodal Image Reconstruction

Chen¹,

Li²,

Ye³

2019

SIAM J. Imaging Sci.

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“…Moreover, further improvement was demonstrated by reconstructing multiple clinical contrasts jointly. This idea was previously investigated for PI‐CS reconstructions where additional sparsity constraints along the contrast dimension were used, and this concept has now also been applied to deep learning . By exploiting the redundancy across the jointly reconstructed contrasts, these techniques enable better image quality than single‐contrast methods.…”

Section: Introductionmentioning

confidence: 99%

Joint multi‐contrast variational network reconstruction (jVN) with application to rapid 2D and 3D imaging

Polak

Cauley

Bilgic̦

et al. 2020

Magnetic Resonance in Med

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Purpose: To improve the image quality of highly accelerated multi-channel MRI data by learning a joint variational network that reconstructs multiple clinical contrasts jointly.Methods: Data from our multi-contrast acquisition was embedded into the variational network architecture where shared anatomical information is exchanged by mixing the input contrasts.Complementary k-space sampling across imaging contrasts and Bunch-Phase/Wave-Encoding were used for data acquisition to improve the reconstruction at high accelerations. At 3T, our joint variational network approach across T1w, T2w and T2-FLAIR-weighted brain scans was tested for retrospective under-sampling at R=6 (2D) and R=4x4 (3D) acceleration. Prospective acceleration was also performed for 3D data where the combined acquisition time for whole brain coverage at 1 mm isotropic resolution across three contrasts was less than three minutes.Results: Across all test datasets, our joint multi-contrast network better preserved fine anatomical details with reduced image-blurring when compared to the corresponding single-contrast reconstructions. Improvement in image quality was also obtained through complementary kspace sampling and Bunch-Phase/Wave-Encoding where the synergistic combination yielded the overall best performance as evidenced by exemplarily slices and quantitative error metrics. Conclusion:By leveraging shared anatomical structures across the jointly reconstructed scans, our joint multi-contrast approach learnt more efficient regularizers which helped to retain natural image appearance and avoid over-smoothing. When synergistically combined with advanced encoding techniques, the performance was further improved, enabling up to R=16-fold acceleration with good image quality. This should help pave the way to very rapid high-resolution brain exams.

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“…Specifically, colour TV (CTV) and group ℓ 1 ‐sparsity are used to exploit common information across contrasts, and individual TV and ℓ 1 ‐sparsity are used to prevent leakage‐of‐features. SIMIT is demonstrated for multi‐contrast imaging, where the resulting optimization problem is solved efficiently via an adaptation of Alternating Direction Method of Multipliers (ADMM) . First, SIMIT is compared against alternative reconstructions that only use individual ℓ 1 ‐sparsity and TV terms (Indiv‐only) or only use the joint terms CTV and group ℓ 1 ‐sparsity (Joint‐only), on a numerical phantom dataset.…”

Section: Introductionmentioning

confidence: 99%

“…SIMIT is demonstrated for multi-contrast imaging, where the resulting optimization problem is solved efficiently via an adaptation 59 of Alternating Direction Method of Multipliers (ADMM). 27,60,61 First, SIMIT is compared against alternative reconstructions that only use individual ℓ 1 -sparsity and TV terms (Indiv-only) 59 or only use the joint terms CTV and group ℓ 1 -sparsity (Joint-only), 62 on a numerical phantom dataset. The phantom only included a single-channel receiver coil to isolate potential leakage artefacts.…”

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confidence: 99%

Simultaneous use of individual and joint regularization terms in compressive sensing: Joint reconstruction of multi‐channel multi‐contrast MRI acquisitions

et al. 2020

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Multi‐contrast images are commonly acquired together to maximize complementary diagnostic information, albeit at the expense of longer scan times. A time‐efficient strategy to acquire high‐quality multi‐contrast images is to accelerate individual sequences and then reconstruct undersampled data with joint regularization terms that leverage common information across contrasts. However, these terms can cause features that are unique to a subset of contrasts to leak into the other contrasts. Such leakage‐of‐features may appear as artificial tissues, thereby misleading diagnosis. The goal of this study is to develop a compressive sensing method for multi‐channel multi‐contrast magnetic resonance imaging (MRI) that optimally utilizes shared information while preventing feature leakage. Joint regularization terms group sparsity and colour total variation are used to exploit common features across images while individual sparsity and total variation are also used to prevent leakage of distinct features across contrasts. The multi‐channel multi‐contrast reconstruction problem is solved via a fast algorithm based on Alternating Direction Method of Multipliers. The proposed method is compared against using only individual and only joint regularization terms in reconstruction. Comparisons were performed on single‐channel simulated and multi‐channel in‐vivo datasets in terms of reconstruction quality and neuroradiologist reader scores. The proposed method demonstrates rapid convergence and improved image quality for both simulated and in‐vivo datasets. Furthermore, while reconstructions that solely use joint regularization terms are prone to leakage‐of‐features, the proposed method reliably avoids leakage via simultaneous use of joint and individual terms, thereby holding great promise for clinical use.

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Vectorial total generalized variation for accelerated multi-channel multi-contrast MRI

Cited by 23 publications

References 32 publications

A Two-Stage Algorithm for Joint Multimodal Image Reconstruction

A Two-Stage Algorithm for Joint Multimodal Image Reconstruction

Joint multi‐contrast variational network reconstruction (jVN) with application to rapid 2D and 3D imaging

Simultaneous use of individual and joint regularization terms in compressive sensing: Joint reconstruction of multi‐channel multi‐contrast MRI acquisitions

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