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      Imaging 4D morphology and dynamics of mitral annulus in humans using cardiac cine MR feature tracking

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          Abstract

          Feature tracking in cine cardiac magnetic resonance (CMR) is a quantitative technique to assess heart structure and function. We investigated 4-dimensional (4D) dynamics and morphology of the mitral annulus (MA) using a novel tracking system based on radially rotational long-axis cine CMR series. A total of 30 normal controls and patients with mitral regurgitation were enrolled. The spatiotemporal changes of the MA were characterized by an in-house developed program. Dynamic and morphological parameters extracted from all 18 radial slices were used as references and were compared with those from subsequently generated sub-datasets with different degrees of sparsity. An excellent agreement was found among all datasets including routine 2-, 3- and 4-chamber views for MA dynamics such as peak systolic velocity (Sm) and mitral annular plane systolic excursion (MAPSE). MA morphology for size and shape was addressed adequately by as few as 6 radial slices, but poorly by only three routine views. Patients with regurgitation showed significantly reduced mitral dynamics and mild annular deformation, which was consistent between three routine views and 18 reference slices. In conclusion, feature tracking cine CMR provided a comprehensive and distinctive profile for 4D MA dynamics and morphology, which may help in studying different cardiac diseases.

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          Array compression for MRI with large coil arrays.

          Arrays with large numbers of independent coil elements are becoming increasingly available as they provide increased signal-to-noise ratios (SNRs) and improved parallel imaging performance. Processing of data from a large set of independent receive channels is, however, associated with an increased memory and computational load in reconstruction. This work addresses this problem by introducing coil array compression. The method allows one to reduce the number of datasets from independent channels by combining all or partial sets in the time domain prior to image reconstruction. It is demonstrated that array compression can be very effective depending on the size of the region of interest (ROI). Based on 2D in vivo data obtained with a 32-element phased-array coil in the heart, it is shown that the number of channels can be compressed to as few as four with only 0.3% SNR loss in an ROI encompassing the heart. With twofold parallel imaging, only a 2% loss in SNR occurred using the same compression factor.
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            Peak early diastolic mitral annulus velocity by tissue Doppler imaging adds independent and incremental prognostic value.

            The aim of this study was to ascertain if left ventricular mitral annulus velocities measured by tissue Doppler imaging (TDI) are more powerful predictors of outcome compared with clinical data and standard Doppler-echocardiographic parameters. Tissue Doppler imaging of basal or mitral annulus velocities provides rapid assessment of ventricular long axis function. But it is not known if TDI-derived velocities in systole and diastole add incremental value and are superior to the standard Doppler-echocardiographic measurements as a predictor of outcome. The study population consisted of 518 subjects, 353 with cardiac disease and 165 normal subjects who had full Doppler two-dimensional-echocardiographic studies with measurement of mitral inflow velocities in early and late diastole, E-wave deceleration time (DT), peak systolic mitral annular velocity (Sm) early and late diastolic mitral annular velocity (Em and Am) by TDI, early diastolic flow propagation velocity, and standard chamber dimensions. All subjects were followed up for two years. The end point was cardiac death. Tissue Doppler imaging mitral annulus systolic and diastolic velocities were all significantly lower in the non-survivors (all p < 0.05) as was DT (p = 0.024). In the Cox model the best predictors of mortality were Em, Sm, Am, left ventricular ejection fraction, left ventricular mass, and left atrial diameter in systole (LADs). By backward stepwise analysis Em and LADs were the strongest predictors. After forcing the TDI measurements into the covariate model with clinical and mitral DT <0.16 s, Em provided significant incremental value for predicting cardiac mortality (p = 0.004). Mitral annulus velocity measured by TDI in early diastole gives incremental predictive power for cardiac mortality compared to clinical data and standard echocardiographic measurements. This easily available measurement adds significant value in the clinical management of cardiac patients.
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              A clinician's guide to tissue Doppler imaging.

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                Author and article information

                Contributors
                zhong.liang@nhcs.com.sg
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                8 January 2018
                8 January 2018
                2018
                : 8
                : 81
                Affiliations
                [1 ]ISNI 0000 0004 0620 9905, GRID grid.419385.2, National Heart Research Institute Singapore, National Heart Centre, ; Singapore, 5 Hospital Drive, 169609 Singapore
                [2 ]Philips Healthcare, 622 Lorong 1, Toa Payoh, 319763 Singapore, Singapore
                [3 ]ISNI 0000 0004 0368 8293, GRID grid.16821.3c, Department of Cardiology, Renji Hospital, School of Medicine, , Shanghai Jiaotong University, ; Shanghai, 200001 People’s Republic of China
                [4 ]ISNI 0000 0004 0385 0924, GRID grid.428397.3, Duke-NUS Medical School, ; 8 College Road, Singapore, 169857 Singapore
                Author information
                http://orcid.org/0000-0003-4570-109X
                Article
                18354
                10.1038/s41598-017-18354-2
                5758818
                29311562
                bf4d72e4-5eda-4ab4-b290-6f86004a4b4f
                © The Author(s) 2017

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 31 May 2017
                : 11 December 2017
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