Three-dimensional analysis of regional left ventricular endocardial curvature from cardiac magnetic resonance images

Maffessanti, Francesco; Lang, Roberto M.; Niel, Johannes; Steringer‐Mascherbauer, Regina; Caiani, Enrico G.; Nesser, Hans‐Joachim; Mor‐Avi, Victor

doi:10.1016/j.mri.2010.11.002

Cited by 14 publications

(6 citation statements)

References 21 publications

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“…This approach has been previously used in the context of 3D analysis of cardiac magnetic resonance images (22). To achieve this, a quadratic polynomial function was fit to the local neighborhood of each point belonging to the LV endocardial surface.…”

Section: Methodsmentioning

confidence: 99%

2D and 3D Echocardiography-Derived Indices of Left Ventricular Function and Shape

Medvedofsky

Maffessanti

Weinert

et al. 2018

JACC: Cardiovascular Imaging

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OBJECTIVES This study hypothesized that left ventricular (LV) ejection fraction (EF) and global longitudinal strain (GLS) derived from 3-dimensional echocardiographic (3DE) images would better predict mortality than those obtained by 2-dimensional echocardiographic (2DE) measurements, and that 3DE-based LV shape analysis may have added prognostic value. BACKGROUND Previous studies have shown that both LVEF and GLS derived from 2DE images predict mortality. Recently, 3DE measurements of these parameters were found to be more accurate and reproducible because of independence of imaging plane and geometric assumptions. Also, 3DE analysis offers an opportunity to accurately quantify LV shape. METHODS We retrospectively studied 416 inpatients (60 18 years of age) referred for transthoracic echocardiography between 2006 and 2010, in whom good-quality 2DE and 3DE images were available. Mortality data through 2016 were collected. Both 2DE and 3DE images were analyzed to measure LVEF and GLS. Additionally, 3DE-derived LV endocardial surface information was analyzed to obtain global shape indices (sphericity and conicity) and regional curvature (anterior, septal, inferior, lateral walls). Cardiovascular (CV) mortality risks related to these indices were determined using Cox regression. RESULTS Of the 416 patients, 208 (50%) died, including 114 (27%) CV-related deaths over a mean follow-up period of 5 3 years. Cox regression revealed that age and body surface area, all 4 LV function indices (2D EF, 3D EF, 2D GLS, 3D GLS), and regional shape indices (septal and inferior wall curvatures) were independently associated with increased risk of CV mortality. GLS was the strongest prognosticator of CV mortality, superior to EF for both 2DE and 3DE analyses, and 2D EF was the weakest among the 4 functional indices. A 1% decrease in GLS magnitude was associated with an 11.3% increase in CV mortality risk. CONCLUSIONS GLS predicts mortality better than EF by both 3DE and 2DE analysis, whereas 3D EF is a better predictor than 2D EF. Also, LV shape indices provide additional risk assessment.

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Section: Methodsmentioning

confidence: 99%

2D and 3D Echocardiography-Derived Indices of Left Ventricular Function and Shape

Medvedofsky

Maffessanti

Weinert

et al. 2018

JACC: Cardiovascular Imaging

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“…Several recent studies have demonstrated the potential use of 3D shape analysis to obtain not only new global indices, such as sphericity and conicity, 21 but also regional shape indices, such as 3D curvature. [22][23][24] Having access to these parameters on a frame-by-frame basis from 3DE images without extensive user interaction and time commitment is likely to prove clinically useful from both diagnostic and prognostic points of view. 25,26 Harnessing this information for potential clinical use may prove beneficial in future studies designed to investigate this aspect of cardiac chamber shape in different disease states.…”

Section: Future Directionsmentioning

confidence: 99%

Machine learning based automated dynamic quantification of left heart chamber volumes

Narang

Mor‐Avi

Prado

et al. 2018

European Heart Journal - Cardiovascular Imaging

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Aims Studies have demonstrated the ability of a new automated algorithm for volumetric analysis of 3D echocardiographic (3DE) datasets to provide accurate and reproducible measurements of left ventricular and left atrial (LV, LA) volumes at end-systole and end-diastole. Recently, this methodology was expanded using a machine learning (ML) approach to automatically measure chamber volumes throughout the cardiac cycle, resulting in LV and LA volume–time curves. We aimed to validate ejection and filling parameters obtained from these curves by comparing them to independent well-validated reference techniques. Methods and results We studied 20 patients referred for cardiac magnetic resonance (CMR) examinations, who underwent 3DE imaging the same day. Volume–time curves were obtained for both LV and LA chambers using the ML algorithm (Philips HeartModel), and independently conventional 3DE volumetric analysis (TomTec), and CMR images (slice-by-slice, frame-by-frame manual tracing). Automatically derived LV and LA volumes and ejection/filling parameters were compared against both reference techniques. Minor manual correction of the automatically detected LV and LA borders was needed in 4/20 and 5/20 cases, respectively. Time required to generate volume–time curves was 35 ± 17 s using ML algorithm, 3.6 ± 0.9 min using conventional 3DE analysis, and 96 ± 14 min using CMR. Volume–time curves obtained by all three techniques were similar in shape and magnitude. In both comparisons, ejection/filling parameters showed no significant inter-technique differences. Bland–Altman analysis confirmed small biases, despite wide limits of agreement. Conclusion The automated ML algorithm can quickly measure dynamic LV and LA volumes and accurately analyse ejection/filling parameters. Incorporation of this algorithm into the clinical workflow may increase the utilization of 3DE imaging.

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“…Echocardiography and cardiac magnetic resonance imaging (MRI) have permitted valuable clinical contributions to the non-invasive diagnosis of cardiac dysfunctions based on myocardial deformation (Claus et al, 2015). However, every heart is different and the variability in geometry and local curvature of the left ventricle (LV) entails different amplitude and timing of regional motion (Bogaert and Rademakers, 2001;Maffessanti et al, 2011). Different normal patterns of LV regional deformation allow a natural synchrony, which does not mean simultaneity, able to create the proper intraventricular pressure gradients (IVPGs) that drive blood motion during both ventricular ejection and ventricular filling.…”

Section: Introductionmentioning

confidence: 99%

On estimating intraventricular hemodynamic forces from endocardial dynamics: A comparative study with 4D flow MRI

Pedrizzetti

Arvidsson

Töger

et al. 2017

Journal of Biomechanics

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Intraventricular pressure gradients or hemodynamic forces, which are their global measure integrated over the left ventricular volume, have a fundamental importance in ventricular function. They may help revealing a sub-optimal cardiac function that is not evident in terms of tissue motion, which is naturally heterogeneous and variable, and can influence cardiac adaptation. However, hemodynamic forces are not utilized in clinical cardiology due to the unavailability of simple non-invasive measurement tools. Hemodynamic forces depend on the intraventricular flow; nevertheless, most of them are imputable to the dynamics of the endocardial flow boundary and to the exchange of momentum across the mitral and aortic orifices. In this study, we introduce a simplified model based on first principles of fluid dynamics that allows estimating hemodynamic forces without knowing the velocity field inside the LV. The model is validated with 3D phase-contrast MRI (known as 4D flow MRI) in 15 subjects, (5 healthy and 10 patients) using the endocardial surface reconstructed from the three standard long-axis projections. Results demonstrate that the model provides consistent estimates for the base-apex component (mean correlation coefficient r=0.77 for instantaneous values and r=0.88 for root mean square) and good estimates of the inferolateral-anteroseptal component (r=0.50 and 0.84, respectively). The present method represents a potential integration to the existing ones quantifying endocardial deformation in MRI and echocardiography to add a physics-based estimation of the corresponding hemodynamic forces. These could help the clinician to early detect sub-clinical diseases and differentiate between different cardiac dysfunctional states.

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Three-dimensional analysis of regional left ventricular endocardial curvature from cardiac magnetic resonance images

Cited by 14 publications

References 21 publications

2D and 3D Echocardiography-Derived Indices of Left Ventricular Function and Shape

2D and 3D Echocardiography-Derived Indices of Left Ventricular Function and Shape

Machine learning based automated dynamic quantification of left heart chamber volumes

On estimating intraventricular hemodynamic forces from endocardial dynamics: A comparative study with 4D flow MRI

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