scholarly journals DeepStrain: A Deep Learning Workflow for the Automated Characterization of Cardiac Mechanics

2021 ◽  
Vol 8 ◽  
Author(s):  
Manuel A. Morales ◽  
Maaike van den Boomen ◽  
Christopher Nguyen ◽  
Jayashree Kalpathy-Cramer ◽  
Bruce R. Rosen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Motion Estimation ◽  
Healthy Subjects ◽  
Strain Analysis ◽  
Cardiac Mechanics ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Cine Mri ◽  
Tagging Mri

Myocardial strain analysis from cinematic magnetic resonance imaging (cine-MRI) data provides a more thorough characterization of cardiac mechanics than volumetric parameters such as left-ventricular ejection fraction, but sources of variation including segmentation and motion estimation have limited its wider clinical use. We designed and validated a fast, fully-automatic deep learning (DL) workflow to generate both volumetric parameters and strain measures from cine-MRI data consisting of segmentation and motion estimation convolutional neural networks. The final motion network design, loss function, and associated hyperparameters are the result of a thorough ad hoc implementation that we carefully planned specific for strain quantification, tested, and compared to other potential alternatives. The optimal configuration was trained using healthy and cardiovascular disease (CVD) subjects (n = 150). DL-based volumetric parameters were correlated (>0.98) and without significant bias relative to parameters derived from manual segmentations in 50 healthy and CVD test subjects. Compared to landmarks manually-tracked on tagging-MRI images from 15 healthy subjects, landmark deformation using DL-based motion estimates from paired cine-MRI data resulted in an end-point-error of 2.9 ± 1.5 mm. Measures of end-systolic global strain from these cine-MRI data showed no significant biases relative to a tagging-MRI reference method. On 10 healthy subjects, intraclass correlation coefficient for intra-scanner repeatability was good to excellent (>0.75) for all global measures and most polar map segments. In conclusion, we developed and evaluated the first end-to-end learning-based workflow for automated strain analysis from cine-MRI data to quantitatively characterize cardiac mechanics of healthy and CVD subjects.

scholarly journals DeepStrain: A Deep Learning Workflow for the Automated Characterization of Cardiac Mechanics

2021 ◽  
Author(s):  
Manuel Morales ◽  
Maaike van den Boomen ◽  
Christopher Nguyen ◽  
Jayashree Kalpathy-Cramer ◽  
Bruce Rosen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Motion Estimation ◽  
Healthy Subjects ◽  
Myocardial Strain ◽  
Strain Analysis ◽  
Cardiac Mechanics ◽  
Cine Mri ◽  
Myocardial Strain Analysis ◽  
Polar Map

Myocardial strain analysis from cinematic magnetic resonance imaging (cine-MRI) data could provide a more thorough characterization of cardiac mechanics than volumetric parameters such as left-ventricular ejection fraction, but sources of variation including segmentation and motion estimation have limited its wide clinical use. We designed and validated a deep learning (DL) workflow to generate both volumetric parameters and strain measures from cine-MRI data, including strain rate (SR) and regional strain polar maps, consisting of segmentation and motion estimation convolutional neural networks developed and trained using healthy and cardiovascular disease (CVD) subjects (n=150). DL-based volumetric parameters were correlated (>0.98) and without significant bias relative to parameters derived from manual segmentations in 50 healthy and CVD subjects. Compared to landmarks manually-tracked on tagging-MRI images from 15 healthy subjects, landmark deformation using DL-based motion estimates from paired cine-MRI data resulted in an endpoint-error of 2.9 (1.5) mm. Measures of end-systolic global strain from these cine-MRI data showed no significant biases relative to a tagging-MRI reference method. On 4 healthy subjects, intraclass correlation coefficient for intrascanner repeatability was excellent (>0.95) for strain, moderate to excellent for SR (0.690-0.963), and good to excellent (0.826-0.994) in most polar map segments. Absolute relative change was within ~5% for strain, within ~10% for SR, and <1% in half of polar map segments. In conclusion, we developed and evaluated a DL-based, end-to-end fully-automatic workflow for global and regional myocardial strain analysis to quantitatively characterize cardiac mechanics of healthy and CVD subjects based on ubiquitously acquired cine-MRI data.

Abstract P345: A MRI Displacement-based Deep-learning Semantic Segmentation Tool For Left-ventricular Longitudinal Strain Analysis In Cardiotoxicity

2021 ◽  
Vol 129 (Suppl_1) ◽  
Author(s):  
Julia Kar ◽  
Michael V Cohen ◽  
Teja Poorsala ◽  
Samuel A McQuiston ◽  
Cheri Revere ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Cancer Patients ◽  
Healthy Subjects ◽  
Longitudinal Strain ◽  
Strain Analysis ◽  
Semantic Segmentation ◽  
Left Ventricular ◽  
Phase Unwrapping ◽  
Breast Cancer Patients

Global longitudinal strain (GLS) computed in the left-ventricle (LV) is an established metric for detecting cardiotoxicity in breast cancer patients treated with antineoplastic agents. The purpose of this study was to develop a novel, MRI-based, deep-learning semantic segmentation tool that automates the phase-unwrapping for LV displacement computation in GLS. Strain analysis via phase-unwrapping was conducted on 30 breast cancer patients and 30 healthy females acquired with the Displacement Encoding with Stimulated Echoes (DENSE) sequence. A ResNet-50 deep convolutional neural network (DCNN) architecture for automated phase-unwrapping, a previously validated ResNet-50 DCNN for chamber quantification and the Radial Point Interpolation Method were used for GLS computation (Figure 1). The DCNN's performance was measured with F1 and Dice scores, and validated in comparison to the robust transport of intensity equation (RTIE) and quality guided phase-unwrapping (QGPU) conventional algorithms. The three techniques were compared by intraclass correlation coefficient with Cronbach’s alpha (C-alpha) index. Classification accuracy with the DCNN was F1 score of 0.92 and Dice score of 0.89. The GLS results from RTIE, QGPU and DCNN were -16.0 ± 2%, -16.1 ± 3% and -15.9 ± 3% (C-alpha = 0.89) for patients and -18.9 ± 3%, -19.0 ± 4% and -18.9 ± 3% (C-alpha = 0.92) for healthy subjects. Comparable validation results from the three techniques show the feasibility of a DCNN-based approach to LV displacement and GLS analysis. The dissimilarities between patients and healthy subjects demonstrate that DCNN-based GLS computation may detect LV abnormalities related to cardiotoxicity.

scholarly journals Detecting Cardiomyopathies in Pregnancy and the Postpartum Period with an Electrocardiogram-Based Deep Learning Model

2021 ◽  
Author(s):  
Demilade A Adedinsewo ◽  
Patrick W Johnson ◽  
Erika J Douglass ◽  
Itzhak Zachi Attia ◽  
Sabrina D Phillips ◽  
...  
Keyword(s):  
Deep Learning ◽  
Natriuretic Peptides ◽  
Postpartum Period ◽  
Learning Model ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Clinical Parameters ◽  
Multivariable Model ◽  
Ventricular Ejection Fraction ◽  
Deep Learning Model

Abstract Aims Cardiovascular disease is a major threat to maternal health, with cardiomyopathy being among the most common acquired cardiovascular diseases during pregnancy and the postpartum period. The aim of our study was to evaluate the effectiveness of an electrocardiogram (ECG)-based deep learning model in identifying cardiomyopathy during pregnancy and the postpartum period. Methods and Results We used an ECG-based deep learning model to detect cardiomyopathy in a cohort of women who were pregnant or in the postpartum period seen at Mayo Clinic. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. We compared the diagnostic probabilities of the deep learning model with natriuretic peptides and a multivariable model consisting of demographic and clinical parameters. The study cohort included 1,807 women; 7%, 10%, and 13% had left ventricular ejection fraction (LVEF) of 35% or less, less than 45%, and less than 50%, respectively. The ECG-based deep learning model identified cardiomyopathy with AUCs of 0.92 (LVEF ≤35%), 0.89 (LVEF &lt;45%), and 0.87 (LVEF &lt;50%). For LVEF of 35% or less, AUC was higher in Black (0.95) and Hispanic (0.98) women compared to White (0.91). Natriuretic peptides and the multivariable model had AUCs of 0.85 to 0.86 and 0.72, respectively. Conclusions An ECG-based deep learning model effectively identifies cardiomyopathy during pregnancy and the postpartum period and outperforms natriuretic peptides and traditional clinical parameters with the potential to become a powerful initial screening tool for cardiomyopathy in the obstetric care setting.

Abstract 13246: The Impact of Body-Mass Index Trajectories During Early Adulthood on Cardiac Mechanics in Middle Age: The CARDIA Study

Circulation ◽  
2014 ◽  
Vol 130 (suppl_2) ◽  
Author(s):  
Sadiya S Khan ◽  
Sanjiv J Shah ◽  
Kiang J Liu ◽  
Cora E Lewis ◽  
Christina Shay ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Young Adulthood ◽  
Cardiac Dysfunction ◽  
Middle Age ◽  
Cardiac Mechanics ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
The Impact ◽  
Baseline Bmi

Introduction: Obesity is a risk factor for left ventricular dysfunction and incident heart failure. We hypothesized that baseline body mass index (BMI) and trajectories in weight change through young adulthood are associated with abnormal cardiac mechanics in middle age. Methods: We examined 2,735 participants from the Coronary Artery Risk Development in Young Adults (CARDIA) study. BMI was calculated at exam years 0, 2, 5, 7, 10, 15, 20, 25. 2D echo was performed with speckle-tracking analysis. Left ventricular ejection fraction (LVEF) and global longitudinal, circumferential, and radial strain (GLS, GCS, GRS, respectively) were measured at y25. Group-based modeling with latent class analysis (PROC TRAJ) was used to identify trajectories in relative changes in BMI (% change in BMI from baseline at each exam). Linear regression examined associations between baseline BMI and trajectory of BMI change and absolute GLS, GCS, and GRS at y25 adjusting for demographics, risk factors, and echo parameters. Results: Mean age at baseline was 25±4 years. Baseline BMI at y0 was significantly associated with mean GLS at y25 (p=0.01), but not GRS or GCS. We identified 4 distinct trajectories of relative BMI change: stable weight (36% of sample), mild increase (40%), moderate increase (18%), and major increase (6%) in weight (Figure). At y25, there was no difference in LVEF across the 4 BMI trajectory groups (P=NS). After adjustment for clinical variables and baseline BMI, absolute GLS was lower in groups with BMI increases (overall P<0.001). GRS and GCS were not significantly different between the groups. Conclusion: In conclusion, baseline BMI and increases in BMI during young adulthood are significantly associated with the presence of subclinical cardiac dysfunction in middle age despite normal EF. This novel characterization of BMI trajectories across young adulthood may assist in improving understanding of the impact of weight gain and obesity on cardiac dysfunction.

Abstract 224: A Deep Learning Approach to Left-Ventricular Chamber Quantification for Fully Automated Three Dimensional Strain Analysis in Cardiotoxicity

2020 ◽  
Vol 127 (Suppl_1) ◽  
Author(s):  
Bryant M Baldwin ◽  
Shane Joseph ◽  
Xiaodong Zhong ◽  
Ranya Kakish ◽  
Cherie Revere ◽  
...  
Keyword(s):  
Deep Learning ◽  
Interpolation Method ◽  
Three Dimensional ◽  
Strain Analysis ◽  
Correlation Coefficients ◽  
Semantic Segmentation ◽  
Left Ventricular ◽  
Breast Cancer Patients ◽  
Left Ventricular Chamber ◽  
Longitudinal Strains

This study investigated MRI and semantic segmentation-based deep-learning (SSDL) automation for left-ventricular chamber quantifications (LVCQ) and low longitudinal strain (LLS) determination, thus eliminating user-bias by providing an automated tool to detect cardiotoxicity (CT) in breast cancer patients treated with antineoplastic agents. Displacement Encoding with Stimulated Echoes-based (DENSE) myocardial images from 26 patients were analyzed with the tool’s Convolution Neural Network with underlying Resnet-50 architecture. Quantifications based on the SSDL tool’s output were for LV end-diastolic diameter (LVEDD), ejection fraction (LVEF), and mass (LVM) (see figure for phase sequence). LLS was analyzed with Radial Point Interpolation Method (RPIM) with DENSE phase-based displacements. LVCQs were validated by comparison to measurements obtained with an existing semi-automated vendor tool (VT) and strains by 2 independent users employing Bland-Altman analysis (BAA) and interclass correlation coefficients estimated with Cronbach’s Alpha (C-Alpha) index. F1 score for classification accuracy was 0.92. LVCQs determined by SSDL and VT were 4.6 ± 0.5 vs 4.6 ± 0.7 cm (C-Alpha = 0.93 and BAA = 0.5 ± 0.5 cm) for LVEDD, 58 ± 5 vs 58 ± 6 % (0.90, 1 ± 5%) for LVEF, 119 ± 17 vs 121 ± 14 g (0.93, 5 ± 8 g) for LV mass, while LLS was 14 ± 4 vs 14 ± 3 % (0.86, 0.2 ± 6%). Hence, equivalent LV dimensions, mass and strains measured by VT and DENSE imaging validate our unique automated analytic tool. Longitudinal strains in patients can then be analyzed without user bias to detect abnormalities for the indication of cardiotoxicity and the need for therapeutic intervention even if LVEF is not affected.

scholarly journals Intraventricular flow patterns during right ventricular apical pacing

Open Heart ◽  
2019 ◽  
Vol 6 (1) ◽  
pp. e001057 ◽  
Author(s):  
Francesco Bianco ◽  
Vincenzo Cicchitti ◽  
Valentina Bucciarelli ◽  
Alvin Chandra ◽  
Enrico Di Girolamo ◽  
...  
Keyword(s):  
Ejection Fraction ◽  
Sinus Rhythm ◽  
Right Ventricular ◽  
Global Longitudinal Strain ◽  
Multivariable Analysis ◽  
Strain Analysis ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Ventricular Ejection Fraction ◽  
Right Ventricular Apical Pacing

ObjectivesTo assess differences in blood flow momentum (BFM) and kinetic energy (KE) dissipation in a model of cardiac dyssynchrony induced by electrical right ventricular apical (RVA) stimulation compared with spontaneous sinus rhythm.MethodsWe cross-sectionally enrolled 12 consecutive patients (mean age 74±8 years, 60% male, mean left ventricular ejection fraction 58%±6 %), within 48 hours from pacemaker (PMK) implantation. Inclusion criteria were: age>18 years, no PMK-dependency, sinus rhythm with a spontaneous narrow QRS at the ECG, preserved ejection fraction (>50%) and a low percentage of PMK-stimulation (<20%). All the participants underwent a complete echocardiographic evaluation, including left ventricular strain analysis and particle image velocimetry.ResultsCompared with sinus rhythm, BFM shifted from 27±3.3 to 34±7.6° (p=0.016), while RVA-pacing was characterised by a 35% of increment in KE dissipation, during diastole (p=0.043) and 32% during systole (p=0.016). In the same conditions, left ventricle global longitudinal strain (LV GLS) significantly decreased from 17±3.3 to 11%±2.8% (p=0.004) during RVA-stimulation. At the multivariable analysis, BFM and diastolic KE dissipation were significantly associated with LV GLS deterioration (Beta Coeff.=0.54, 95% CI 0.07 to 1.00, p=0.034 and Beta Coeff.=0.29, 95% CI 0.02 to 0.57, p=0.049, respectively).ConclusionsIn RVA-stimulation, BFM impairment and KE dissipation were found to be significantly associated with LV GLS deterioration, when controlling for potential confounders. Such changes may favour the onset of cardiac remodelling and sustain heart failure.

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