Dataset Information

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

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.

SUBMITTER: Leng S

PROVIDER: S-EPMC5758818 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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

Leng Shuang S Zhang Shuo S Jiang Meng M Zhao Xiaodan X Wu Rui R Allen John J He Ben B Tan Ru San RS Zhong Liang L

Scientific reports 20180108 1

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 pa ...[more]

PMID: 29311562

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Project description:BACKGROUND:In mitral valve dysfunction, noninvasive measurement of transmitral blood flow is an important clinical examination. Flow imaging of the mitral valve, however, is challenging, since it moves in and out of the image plane during the cardiac cycle. PURPOSE:To more accurately measure mitral flow, a slice-following MRI phase contrast sequence is proposed. This study aimed to implement such a sequence, validate its slice-following functionality in a phantom and healthy subjects, and test its feasibility in patients with mitral valve dysfunction. STUDY TYPE:Prospective. PHANTOM AND SUBJECTS:The slice-following functionality was validated in a cone-shaped phantom by measuring the depicted slice radius. Sixteen healthy subjects and 10 mitral valve dysfunction patients were enrolled at two sites. FIELD STRENGTH/SEQUENCE:1.5T and 3T gradient echo cine phase contrast. ASSESSMENT:A single breath-hold retrospectively gated sequence using offline feature-tracking of the mitral valve was developed. Valve displacements were measured and imported to the scanner, allowing the slice position to change dynamically based on the cardiac phase. Mitral valve imaging was performed with slice-following and static imaging planes. Validation was performed by comparing mitral stroke volume with planimetric and aortic stroke volume. STATISTICAL TESTS:Measurements were compared using linear regression, Pearson's R, parametric paired t-tests, Bland-Altman analysis, and intraclass correlation coefficient (ICC). RESULTS:Phantom experiments confirmed accurate slice displacements. Slice-following was feasible in all subjects, yielding physiologically accurate mitral flow patterns. In healthy subjects, mitral and aortic stroke volumes agreed, with ICC = 0.72 and 0.90 for static and slice-following planes; with bias ±1 SDs 23.2?±?13.2 mls and 8.4?±?10.8 mls, respectively. Agreement with planimetry was stronger, with ICC = 0.84 and 0.96; bias ±1 SDs 13.7?±?13.7 mls and -2.0?±?8.8 mls for static and slice-following planes, respectively. DATA CONCLUSION:Slice-following outperformed the conventional sequence and improved the accuracy of transmitral flow, which is important for assessment of diastolic function and mitral regurgitation. LEVEL OF EVIDENCE:2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:1412-1421.

Project description:Purpose To evaluate myocardial strain and circumferential transmural strain difference (cTSD; the difference between epicardial and endocardial circumferential strain) in a genotyped cohort with hypertrophic cardiomyopathy (HCM) and to explore correlations between cTSD and other anatomic and functional markers of disease status. Left ventricular (LV) dysfunction may indicate early disease in preclinical HCM (sarcomere mutation carriers without LV hypertrophy). Cardiac MRI feature tracking may be used to evaluate myocardial strain in carriers of HCM sarcomere mutation. Materials and Methods Participants with HCM and their family members participated in a prospective, multicenter, observational study (HCMNet). Genetic testing was performed in all participants. Study participants underwent cardiac MRI with temporal resolution at 40 msec or less. LV myocardial strain was analyzed by using feature-tracking software. Circumferential strain was measured at the epicardial and endocardial surfaces; their difference yielded the circumferential transmural strain difference (cTSD). Multivariable analysis to predict HCM status was performed by using multinomial logistic regression adjusting for age, sex, and LV parameters. Results Ninety-nine participants were evaluated (23 control participants, 34 participants with preclinical HCM [positive for sarcomere mutation and negative for LV hypertrophy], and 42 participants with overt HCM [positive for sarcomere mutation and negative for LV hypertrophy]). The average age was 25 years ± 11 and 44 participants (44%) were women. Maximal LV wall thickness was 9.5 mm ± 1.4, 9.8 mm ± 2.2, and 16.1 mm ± 5.3 in control participants, participants with preclinical HCM (P = .496 vs control participants), and participants with overt HCM (P < .001 vs control participants), respectively. cTSD for control participants, preclinical HCM, and overt HCM was 14% ± 4, 17% ± 4, and 22% ± 7, respectively (P < .01 for all comparisons). In multivariable models (controlling for septal thickness and log-transformed N-terminal brain-type natriuretic peptide), cTSD was predictive of preclinical and overt HCM disease status (P < .01). Conclusion Cardiac MRI feature tracking identifies myocardial dysfunction not only in participants with overt hypertrophic cardiomyopathy, but also in carriers of sarcomere mutation without left ventricular hypertrophy, suggesting that contractile abnormalities are present even when left ventricular wall thickness is normal. © RSNA, 2018 Online supplemental material is available for this article.

Project description:PURPOSE:Cardiac image segmentation is a critical process for generating personalized models of the heart and for quantifying cardiac performance parameters. Fully automatic segmentation of the left ventricle (LV), the right ventricle (RV), and the myocardium from cardiac cine MR images is challenging due to variability of the normal and abnormal anatomy, as well as the imaging protocols. This study proposes a multi-task learning (MTL)-based regularization of a convolutional neural network (CNN) to obtain accurate segmenation of the cardiac structures from cine MR images. METHODS:We train a CNN network to perform the main task of semantic segmentation, along with the simultaneous, auxiliary task of pixel-wise distance map regression. The network also predicts uncertainties associated with both tasks, such that their losses are weighted by the inverse of their corresponding uncertainties. As a result, during training, the task featuring a higher uncertainty is weighted less and vice versa. The proposed distance map regularizer is a decoder network added to the bottleneck layer of an existing CNN architecture, facilitating the network to learn robust global features. The regularizer block is removed after training, so that the original number of network parameters does not change. The trained network outputs per-pixel segmentation when a new patient cine MR image is provided as an input. RESULTS:We show that the proposed regularization method improves both binary and multi-class segmentation performance over the corresponding state-of-the-art CNN architectures. The evaluation was conducted on two publicly available cardiac cine MRI datasets, yielding average Dice coefficients of 0.84 ± 0.03 and 0.91 ± 0.04. We also demonstrate improved generalization performance of the distance map regularized network on cross-dataset segmentation, showing as much as 42% improvement in myocardium Dice coefficient from 0.56 ± 0.28 to 0.80 ± 0.14. CONCLUSIONS:We have presented a method for accurate segmentation of cardiac structures from cine MR images. Our experiments verify that the proposed method exceeds the segmentation performance of three existing state-of-the-art methods. Furthermore, several cardiac indices that often serve as diagnostic biomarkers, specifically blood pool volume, myocardial mass, and ejection fraction, computed using our method are better correlated with the indices computed from the reference, ground truth segmentation. Hence, the proposed method has the potential to become a non-invasive screening and diagnostic tool for the clinical assessment of various cardiac conditions, as well as a reliable aid for generating patient specific models of the cardiac anatomy for therapy planning, simulation, and guidance.

Project description:ObjectivesLongitudinal hemodynamic follow-up is important in the management of pulmonary hypertension (PH). This study aimed to evaluate the potential of MR 4-dimensional (4D) flow imaging to predict changes in the mean pulmonary arterial pressure (mPAP) during serial investigations.MethodsForty-four adult patients with PH or at risk of developing PH repeatedly underwent routine right heart catheterization (RHC) and near-term MR 4D flow imaging of the main pulmonary artery. The duration of vortical blood flow along the main pulmonary artery was evaluated from MR 4D velocity fields using prototype software and converted to an MR 4D flow imaging-based mPAP estimate (mPAPMR) by a previously established model. The relationship of differences between RHC-derived baseline and follow-up mPAP values (ΔmPAP) to corresponding differences in mPAPMR (ΔmPAPMR) was analyzed by means of regression and Bland-Altman analysis; the diagnostic performance of ΔmPAPMR in predicting mPAP increases or decreases was investigated by ROC analysis.ResultsAreas under the curve for the prediction of mPAP increases and decreases were 0.92 and 0.93, respectively. With the natural cutoff ΔmPAPMR = 0 mmHg, mPAP increases (decreases) were predicted with an accuracy, sensitivity, and specificity of 91% (91%), 85% (89%), and 94% (92%), respectively. For patients in whom 4D flow allowed a point estimate of mPAP (mPAP > 16 mmHg), ΔmPAPMR correlated strongly with ΔmPAP (r = 0.91) and estimated ΔmPAP bias-free with a standard deviation of 5.1 mmHg.ConclusionsMR 4D flow imaging allows accurate non-invasive prediction and quantification of mPAP changes in adult patients with PH or at risk of developing PH.Trial registrationClinicalTrials.gov identifier: NCT00575692 and NCT01725763 KEY POINTS: • MR 4D flow imaging allows accurate non-invasive prediction of mean pulmonary arterial pressure increases and decreases in adult patients with or at risk of developing pulmonary hypertension. • In adult patients with mean pulmonary arterial pressure > 16 mmHg, MR 4D flow imaging allows estimation of longitudinal mean pulmonary arterial pressure changes without bias with a standard deviation of 5.1 mmHg.

Project description:PurposeTo compare the accuracy of semiautomated flow tracking with that of semiautomated valve tracking in the quantification of mitral valve (MV) regurgitation from clinical four-dimensional (4D) flow MRI data obtained in patients with mild, moderate, or severe MV regurgitation.Materials and methodsThe 4D flow MRI data were retrospectively collected from 30 patients (21 men; mean age, 61 years ± 10 [standard deviation]) who underwent 4D flow MRI from 2006 to 2016. Ten patients had mild MV regurgitation, nine had moderate MV regurgitation, and 11 had severe MV regurgitation, as diagnosed by using semiquantitative echocardiography. The regurgitant volume (Rvol) across the MV was obtained using three methods: indirect quantification of Rvol (RvolINDIRECT), semiautomated quantification of Rvol using valve tracking (RvolVALVE), and semiautomated quantification of Rvol using flow tracking (RvolFLOW). A second observer repeated the measurements. Aortic valve flow was quantified as well to test for intervalve consistency. The Wilcoxon signed rank test, orthogonal regression, Bland-Altman analysis, and coefficients of variation were used to assess agreement among measurements and between observers.ResultsRvolFLOW was higher (median, 24.8 mL; interquartile range [IQR], 14.3-45.7 mL) than RvolVALVE (median, 9.9 mL; IQR, 6.0-16.9 mL; P < .001). Both RvolFLOW and RvolVALVE differed significantly from RvolINDIRECT (median, 19.1 mL; IQR, 4.1-47.5 mL; P = .03). RvolFLOW agreed more with RvolINDIRECT (ŷ = 0.78x + 12, r = 0.88) than with RvolVALVE (ŷ = 0.16x + 8.1, r = 0.53). Bland-Altman analysis revealed underestimation of RvolVALVE in severe MV regurgitation. Interobserver agreement was excellent for RvolFLOW (r = 0.95, coefficient of variation = 27%) and moderate for RvolVALVE (r = 0.72, coefficient of variation = 57%). Orthogonal regression demonstrated better intervalve consistency for flow tracking (ŷ = 1.2x - 13.4, r = 0.82) than for valve tracking (ŷ = 2.7x - 92.4, r = 0.67).ConclusionFlow tracking enables more accurate 4D flow MRI-derived MV regurgitation quantification than valve tracking in terms of agreement with indirect quantification and intervalve consistency, particularly in severe MV regurgitation.Supplemental material is available for this article.© RSNA, 2020.

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Dataset Information

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

Publications

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

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