Dynamic cardiac MRI reconstruction using motion aligned locally low rank tensor (MALLRT)

Liu, Fan; Li, Dongxiao; Jin, Xinyu; Qiu, Wenyuan; Xia, Qi; Sun, Bin

doi:10.1016/j.mri.2019.07.002

Cited by 31 publications

(20 citation statements)

References 38 publications

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“…The inclusion of an explicit motion alignment (based on image registration) in a locally low-rank reconstruction has been recently shown for 2D cardiac CINE. 34 However, as shown in Supporting Information Figure S2 and in Ref. 28, the combination of compression on local and nonlocal scales (as in PROST/HD-PROST) outperforms local low rank methods by exploiting nonlocal redundant image content.…”

Section: Discussionmentioning

confidence: 97%

“…The inclusion of an explicit motion alignment (based on image registration) in a locally low‐rank reconstruction has been recently shown for 2D cardiac CINE 34 . However, as shown in Supporting Information Figure and in Ref.…”

Section: Discussionmentioning

confidence: 99%

“…The proposed MB‐PROST jointly reconstructs motion‐resolved multi‐bin 3D images by adding an implicit temporal constraint and thus taking advantage of the common features present in adjacent cardiac phases through multi‐bin patch selection. Global and local low‐rank approaches have been previously proposed to exploit redundancies in the temporal cardiac dimension 34‐37 . A comparison of the spatiotemporal compression achieved with global low‐rank, local low‐rank, PROST, and the proposed MB‐PROST is examined in Supporting Information Figure , showing that higher compression can be achieved with MB‐PROST.…”

Section: Methodsmentioning

confidence: 99%

“…Global and local low-rank approaches have been previously proposed to exploit redundancies in the temporal cardiac dimension. [34][35][36][37] A comparison of the spatiotemporal compression achieved with global low-rank, local low-rank, PROST, and the proposed MB-PROST is examined in Supporting Information Figure S2, showing that higher compression can be achieved with MB-PROST.…”

Section: Mb-prostmentioning

confidence: 99%

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Isotropic 3D Cartesian single breath‐hold CINE MRI with multi‐bin patch‐based low‐rank reconstruction

Küstner

Bustin

Jaubert

et al. 2020

Magnetic Resonance in Med

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Purpose To develop a novel acquisition and reconstruction framework for isotropic 3D Cartesian cardiac CINE within a single breath‐hold for left ventricle (LV) and whole‐heart coverage. Methods A variable‐density Cartesian acquisition with spiral profile ordering, out‐inward sampling, and acquisition‐adaptive alternating tiny golden/golden angle increment between spiral arms is proposed to provide incoherent and nonredundant sampling within and among cardiac phases. A novel multi‐bin patch‐based low‐rank reconstruction, named MB‐PROST, is proposed to exploit redundant information on a local (within a patch), nonlocal (similar patches within a spatial neighborhood), and temporal (among all cardiac phases) scale with an implicit motion alignment among patches. The proposed multi‐bin patch‐based low‐rank reconstruction reconstruction is compared against compressed sensing reconstruction, whereas LV function parameters derived from the proposed 3D CINE framework are compared against those estimated from conventional multislice 2D CINE imaging in 10 healthy subjects and 15 patients. Results The proposed framework provides 3D cardiac CINE images with high spatial (1.9 mm3) and temporal resolution (˜50 ms) in a single breath‐hold of ˜20 s for LV and ˜26 s for whole‐heart coverage in healthy subjects. Shorter breath‐hold durations of ˜13 to 15 s are feasible for LV coverage with slightly anisotropic resolution (1.9 × 1.9 × 2.5 mm) in patients. LV function parameters derived from 3D CINE were in good agreement with 2D CINE, with a bias of −0.1 mL/0.1 mL, −0.9 mL/−1.0 mL, −0.1%/−0.8%; and confidence intervals of ±1.7 mL/±3.7 mL, ±1.2 mL/±2.6 mL, and ±1.2%/±3.6% (10 healthy subjects/15 patients) for end‐systolic volume, end‐diastolic volume, and ejection fraction, respectively. Conclusion The proposed framework enables 3D isotropic cardiac CINE in a single breath‐hold scan of ˜20 s/˜26 s for LV/whole‐heart coverage, showing good agreement with clinical 2D CINE scans in terms of LV functional assessment.

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Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Mb-prostmentioning

confidence: 99%

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Isotropic 3D Cartesian single breath‐hold CINE MRI with multi‐bin patch‐based low‐rank reconstruction

Küstner

Bustin

Jaubert

et al. 2020

Magnetic Resonance in Med

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“…Undersampling introduces violations of the Nyquist sampling theorem and may cause aliasing and blurring issues by direct inverse Fourier transform [3,29]. To solve these issues, prior knowledges are normally incorporated in the reconstruction formulation as regulations [39,16,17,13]. Specifically, through the support of the well-known compressed sensing (CS) theory, researchers have developed a series of dynamic image reconstruction methods by exploiting either spatial or temporal redundancy or both with different sampling patterns.…”

mentioning

confidence: 99%

LANTERN: Learn analysis transform network for dynamic magnetic resonance imaging

Wang¹,

Chen²,

Xiao³

et al. 2021

IPI

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This paper proposes to learn analysis transform network for dynamic magnetic resonance imaging (LANTERN). Integrating the strength of CS-MRI and deep learning, the proposed framework is highlighted in three components: (i) The spatial and temporal domains are sparsely constrained by adaptively trained convolutional filters; (ii) We introduce an end-to-end framework to learn the parameters in LANTERN to solve the difficulty of parameter selection in traditional methods; (iii) Compared to existing deep learning reconstruction methods, our experimental results show that our paper has encouraging capability in exploiting the spatial and temporal redundancy of dynamic MR images. We performed quantitative and qualitative analysis of cardiac reconstructions at different acceleration factors (2×-11×) with different undersampling patterns. In comparison with two state-of-the-art methods, experimental results show that our method achieved encouraging performances.

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Accelerated brain tumor dynamic contrast‐enhanced MRI using Adaptive Pharmaco‐Kinetic Model Constrained method

Liu

Jin

et al. 2021

Int J Imaging Syst Tech

Self Cite

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In brain tumor, dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) spatiotemporally resolved high‐quality reconstruction which is required for quantitative analysis of some physiological characteristics of brain tissue. By exploiting some kind of sparsity priori, compressed sensing methods can achieve high spatiotemporal DCE‐MRI image reconstruction from undersampled k‐space data. Recently, as a kind of priori information about the contrast agent (CA) concentration dynamics, Pharmacokinetic (PK) models have been explored for undersampled DCE‐MRI reconstruction. This paper presents a novel dictionary learning‐based reconstruction method with Adaptive Pharmaco‐Kinetic Model Constraints (APKMC). In APKMC, the priori knowledge about CA dynamics is incorporated into a novel dictionary, which consists of PK model‐based atoms and adaptive atoms. The PK atoms are constructed based on Patlak model and K‐SVD dimension reduction algorithm, and the adaptive ones are used to resolve PK model inconsistencies. To solve APKMC, an optimization algorithm based on variable splitting and alternating iterative optimization is presented. The proposed method has been validated on three brain tumor DCE‐MRI data sets by comparing with two state‐of‐the‐art methods. As demonstrated by the quantitative and qualitative analysis of results, APKMC achieved substantially better quality in the reconstruction of brain DCE‐MRI images, as well as in the reconstruction of PK model parameter maps.

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Dynamic cardiac MRI reconstruction using motion aligned locally low rank tensor (MALLRT)

Cited by 31 publications

References 38 publications

Isotropic 3D Cartesian single breath‐hold CINE MRI with multi‐bin patch‐based low‐rank reconstruction

Isotropic 3D Cartesian single breath‐hold CINE MRI with multi‐bin patch‐based low‐rank reconstruction

LANTERN: Learn analysis transform network for dynamic magnetic resonance imaging

Accelerated brain tumor dynamic contrast‐enhanced MRI using Adaptive Pharmaco‐Kinetic Model Constrained method

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