Dataset Information

3D Myocardial Scar Prediction Model Derived from Multimodality Analysis of Electromechanical Mapping and Magnetic Resonance Imaging.

ABSTRACT: Many cardiac catheter interventions require accurate discrimination between healthy and infarcted myocardia. The gold standard for infarct imaging is late gadolinium-enhanced MRI (LGE-MRI), but during cardiac procedures electroanatomical or electromechanical mapping (EAM or EMM, respectively) is usually employed. We aimed to improve the ability of EMM to identify myocardial infarction by combining multiple EMM parameters in a statistical model. From a porcine infarction model, 3D electromechanical maps were 3D registered to LGE-MRI. A multivariable mixed-effects logistic regression model was fitted to predict the presence of infarct based on EMM parameters. Furthermore, we correlated feature-tracking strain parameters to EMM measures of local mechanical deformation. We registered 787 EMM points from 13 animals to the corresponding MRI locations. The mean registration error was 2.5?±?1.16 mm. Our model showed a strong ability to predict the presence of infarction (C-statistic?=?0.85). Strain parameters were only weakly correlated to EMM measures. The model is accurate in discriminating infarcted from healthy myocardium. Unipolar and bipolar voltages were the strongest predictors.

SUBMITTER: van den Broek HT

PROVIDER: S-EPMC6854049 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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3D Myocardial Scar Prediction Model Derived from Multimodality Analysis of Electromechanical Mapping and Magnetic Resonance Imaging.

van den Broek Hans Thijs HT Wenker Steven S van de Leur Rutger R Doevendans Pieter A PA Chamuleau Steven A J SAJ van Slochteren Frebus J FJ van Es René R

Journal of cardiovascular translational research 20190723 6

Many cardiac catheter interventions require accurate discrimination between healthy and infarcted myocardia. The gold standard for infarct imaging is late gadolinium-enhanced MRI (LGE-MRI), but during cardiac procedures electroanatomical or electromechanical mapping (EAM or EMM, respectively) is usually employed. We aimed to improve the ability of EMM to identify myocardial infarction by combining multiple EMM parameters in a statistical model. From a porcine infarction model, 3D electromechanic ...[more]

PMID: 31338795

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Project description:BackgroundMyocardial fibrosis is a common hallmark of many diseases of the heart. Late gadolinium enhanced MRI is a powerful tool to image replacement fibrosis after myocardial infarction (MI). Interstitial fibrosis can be assessed indirectly from an extracellular volume fraction measurement using contrast-enhanced T1 mapping. Detection of short T2* species resulting from fibrotic tissue may provide an attractive non-contrast-enhanced alternative to directly visualize the presence of both replacement and interstitial fibrosis.ObjectiveTo goal of this paper was to explore the use of a T2*-weighted radial sequence for the visualization of fibrosis in mouse heart.MethodsC57BL/6 mice were studied with MI (n = 20, replacement fibrosis), transverse aortic constriction (TAC) (n = 18, diffuse fibrosis), and as control (n = 10). 3D center-out radial T2*-weighted images with varying TE were acquired in vivo and ex vivo (TE = 21 ?s-4 ms). Ex vivo T2*-weighted signal decay with TE was analyzed using a 3-component model. Subtraction of short- and long-TE images was used to highlight fibrotic tissue with short T2*. The presence of fibrosis was validated using histology and correlated to MRI findings.ResultsDetailed ex vivo T2*-weighted signal analysis revealed a fast (T2*fast), slow (T2*slow) and lipid (T2*lipid) pool. T2*fast remained essentially constant. Infarct T2*slow decreased significantly, while a moderate decrease was observed in remote tissue in post-MI hearts and in TAC hearts. T2*slow correlated with the presence of diffuse fibrosis in TAC hearts (r = 0.82, P = 0.01). Ex vivo and in vivo subtraction images depicted a positive contrast in the infarct co-localizing with the scar. Infarct volumes from histology and subtraction images linearly correlated (r = 0.94, P<0.001). Region-of-interest analysis in the in vivo post-MI and TAC hearts revealed significant T2* shortening due to fibrosis, in agreement with the ex vivo results. However, in vivo contrast on subtraction images was rather poor, hampering a straightforward visual assessment of the spatial distribution of the fibrotic tissue.

Project description:Background:Current magnetic resonance imaging (MRI) of pancreatic disease is qualitative in nature. Quantitative imaging offers several advantages, including increased reproducibility and sensitivity to detect mild or diffuse disease. The role of multiparametric mapping MRI in characterizing various tissue types in pancreatic disease such as chronic pancreatitis (CP) and pancreatic ductal adenocarcinoma (PDAC) has rarely been evaluated. Purpose:To evaluate the feasibility of multiparametric mapping [T1, T2, and apparent diffusion coefficient (ADC)] in defining tissue characteristics that occur in CP and PDAC to improve disease diagnosis. Materials and Methods:Pancreatic MRI was performed in 17 patients with PDAC undergoing therapy, 7 patients with CP, and 29 healthy volunteers with no pancreatic disease. T1 modified Look-Locker Inversion Recovery (T1 MOLLI), T2-prepared gradient-echo, and multi-slice single-shot echo-planar diffusion weighted imaging (SS-EPI DWI) sequences were used for data acquisition. Regions of interest (ROIs) of pancreas in PDAC, CP, and control subjects were outlined by an experienced radiologist. One-way analysis of variance (ANOVA) was used to compare the difference between groups and regions of the pancreas, and Tukey tests were used for multiple comparison testing within groups. Receiver operator characteristic (ROC) curves were analyzed, and the areas under the curves (AUCs) were calculated using single parameter and combined parameters, respectively. Results:T1, T2, and ADC values of the entire pancreas among PDAC, CP, and control subjects; and between upstream and downstream portions of the pancreas in PDAC patients were all significantly different (p < 0.05). The AUC values were 0.90 for T1, 0.55 for T2, and 0.71 for ADC for independent prediction of PDAC. By combining T1, T2, and ADC, the AUC value was 0.94 (sensitivity 91.54%, specificity 85.81%, 95% CI: 0.92-0.96), which yielded higher accuracy than any one parameter only (p < 0.001). Conclusion:Multiparametric mapping MRI is feasible for the evaluation of the differences between PDAC, CP, and normal pancreas tissues. The combination of multiple parameters of T1, T2, and ADC provides a higher accuracy than any single parameter alone in tissue characterization of the pancreas.

Dataset Information

3D Myocardial Scar Prediction Model Derived from Multimodality Analysis of Electromechanical Mapping and Magnetic Resonance Imaging.

Publications

3D Myocardial Scar Prediction Model Derived from Multimodality Analysis of Electromechanical Mapping and Magnetic Resonance Imaging.

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