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Accelerated free-breathing 3D T1ρ cardiovascular magnetic resonance using multicoil compressed sensing

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      Abstract

      Background

      Endogenous contrast T1ρ cardiovascular magnetic resonance (CMR) can detect scar or infiltrative fibrosis in patients with ischemic or non-ischemic cardiomyopathy. Existing 2D T1ρ techniques have limited spatial coverage or require multiple breath-holds. The purpose of this project was to develop an accelerated, free-breathing 3D T1ρ mapping sequence with whole left ventricle coverage using a multicoil, compressed sensing (CS) reconstruction technique for rapid reconstruction of undersampled k-space data.

      Methods

      We developed a cardiac- and respiratory-gated, free-breathing 3D T1ρ sequence and acquired data using a variable-density k-space sampling pattern (A = 3). The effect of the transient magnetization trajectory, incomplete recovery of magnetization between T1ρ-preparations (heart rate dependence), and k-space sampling pattern on T1ρ relaxation time error and edge blurring was analyzed using Bloch simulations for normal and chronically infarcted myocardium. Sequence accuracy and repeatability was evaluated using MnCl 2 phantoms with different T1ρ relaxation times and compared to 2D measurements. We further assessed accuracy and repeatability in healthy subjects and compared these results to 2D breath-held measurements.

      Results

      The error in T1ρ due to incomplete recovery of magnetization between T1ρ-preparations was T1ρ healthy = 6.1% and T1ρ infarct = 10.8% at 60 bpm and T1ρ healthy = 13.2% and T1ρ infarct = 19.6% at 90 bpm. At a heart rate of 60 bpm, error from the combined effects of readout-dependent magnetization transients, k-space undersampling and reordering was T1ρ healthy = 12.6% and T1ρ infarct = 5.8%. CS reconstructions had improved edge sharpness (blur metric = 0.15) compared to inverse Fourier transform reconstructions (blur metric = 0.48). There was strong agreement between the mean T1ρ estimated from the 2D and accelerated 3D data ( R 2 = 0.99; P < 0.05) acquired on the MnCl 2 phantoms. The mean R1ρ estimated from the accelerated 3D sequence was highly correlated with MnCl 2 concentration (R 2 = 0.99; P < 0.05). 3D T1ρ acquisitions were successful in all human subjects. There was no significant bias between undersampled 3D T1ρ and breath-held 2D T1ρ (mean bias = 0.87) and the measurements had good repeatability (COV 2D = 6.4% and COV 3D = 7.1%).

      Conclusions

      This is the first report of an accelerated, free-breathing 3D T1ρ mapping of the left ventricle. This technique may improve non-contrast myocardial tissue characterization in patients with heart disease in a scan time appropriate for patients.

      Electronic supplementary material

      The online version of this article (10.1186/s12968-018-0507-2) contains supplementary material, which is available to authorized users.

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      Most cited references 47

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        Sparse MRI: The application of compressed sensing for rapid MR imaging.

        The sparsity which is implicit in MR images is exploited to significantly undersample k-space. Some MR images such as angiograms are already sparse in the pixel representation; other, more complicated images have a sparse representation in some transform domain-for example, in terms of spatial finite-differences or their wavelet coefficients. According to the recently developed mathematical theory of compressed-sensing, images with a sparse representation can be recovered from randomly undersampled k-space data, provided an appropriate nonlinear recovery scheme is used. Intuitively, artifacts due to random undersampling add as noise-like interference. In the sparse transform domain the significant coefficients stand out above the interference. A nonlinear thresholding scheme can recover the sparse coefficients, effectively recovering the image itself. In this article, practical incoherent undersampling schemes are developed and analyzed by means of their aliasing interference. Incoherence is introduced by pseudo-random variable-density undersampling of phase-encodes. The reconstruction is performed by minimizing the l(1) norm of a transformed image, subject to data fidelity constraints. Examples demonstrate improved spatial resolution and accelerated acquisition for multislice fast spin-echo brain imaging and 3D contrast enhanced angiography. (c) 2007 Wiley-Liss, Inc.
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          The Split Bregman Method for L1-Regularized Problems

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            Author and article information

            Affiliations
            [1 ]ISNI 0000 0004 1936 8972, GRID grid.25879.31, Department of Radiology, , Perelman School of Medicine, University of Pennsylvania, ; Philadelphia, PA 19104 USA
            [2 ]ISNI 0000 0004 1936 8972, GRID grid.25879.31, Department of Bioengineering, , University of Pennsylvania, ; Philadelphia, PA 19104 USA
            [3 ]ISNI 0000 0004 1936 8972, GRID grid.25879.31, Department of Medicine, , University of Pennsylvania, ; Philadelphia, PA 19104 USA
            Contributors
            srikant.iyer@uphs.upenn.edu
            moonbri@seas.upenn.edu
            ehwuang@seas.upenn.edu
            yuchi.han@uphs.upenn.edu
            micsolo@seas.upenn.edu
            harold.litt@uphs.upenn.edu
            witschey@pennmedicine.upenn.edu
            Journal
            J Cardiovasc Magn Reson
            J Cardiovasc Magn Reson
            Journal of Cardiovascular Magnetic Resonance
            BioMed Central (London )
            1097-6647
            1532-429X
            10 January 2019
            10 January 2019
            2019
            : 21
            30626437
            6327532
            507
            10.1186/s12968-018-0507-2
            © The Author(s). 2019

            Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

            Funding
            Funded by: FundRef http://dx.doi.org/10.13039/100000050, National Heart, Lung, and Blood Institute;
            Award ID: R00-HL108157
            Award Recipient :
            Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
            Award ID: UL1TR001878
            Award Recipient :
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            Research
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            © The Author(s) 2019

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