10:30
The heart
Chair: Richard Lopata
10:30
15 mins
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Blood Flow Patterns in a Model of the Human Heart
Fei Xu, Sasa Kenjeres
Abstract: The aortic root is the region which connects the left ventricle to the ascending aorta. It is the main artery which delivers oxygenated blood to the rest of the body.
The current work aims at providing a credible and cost-effective finite-volume based numerical simulations of the flow patterns and pressure within an aortic root model with the bi-leaflet mechanical valve in realistic conditions.
At the first stage, the dynamics of the bi-leaflet is simulated as a result of the fluid-structure interaction (FSI) in which leaflets are considered as solid moving objects with a limited degree of freedom. The chimera overset grid technique has been applied for the dynamic meshing. Such method has the main advantage of maintaining a high mesh quality and near-wall-refinement during the simulation. At the second stage, after applying the kinematics of the leaflets from the first stage, different numerical models have been applied: RANS, DES, LES and quasi-DNS.
At the second stage, dynamic mesh smoothing and remeshing technique has been used for the simulations since it proved to be considerably more time efficient (approximately 10 times) compared to the overset grid method.
The numerically calculated streamwise velocity at two locations and five characteristic time instants over a heartbeat cycle have been compared with available PIV measurements. Obtained results are in a good agreement with experimental data. Furthermore, the vorticity as well as the velocity contours have been compared between different simulations at the central vertical plane. Additionally, energy spectra of the velocity time sequences at five monitoring locations have been analyzed to identify the turbulent/laminar regions in the flow field.
The result provided a detailed insights into energetics of the instantaneous flow features of the aortic root model with realistic conditions.
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10:45
15 mins
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Tomographic PIV in Left Ventricle Phantom for 4D Flow MRI and 4D Echo-PIV Validation
Hicham Saaid, Jason Voorneveld, Christiaan Schinkel, Sasa Kenjeres, Johan G. Bosch, Jos Westenberg, Frank Gijsen, Patrick Segers, Pascal Verdonck, Tom Claessens
Abstract: Previous works have suggested clinical parameters based on left ventricle (LV) flow dynamics as potential early-stage indicators for cardiac health. These parameters were derived from interdisciplinary flow studies relying on medical imaging methods, in vitro experiments using optical flow measurement techniques and/or computational fluid mechanics simulations. Until now, flow measurements were primarily two dimensional, while it is well agreed that LV flow analyses would highly benefit from a volumetric measurement technique to capture the complex spatiotemporal behaviour of the LV flow.
In view of this, the goal of our work is twofold: firstly, showing the feasibility of a full-volumetric particle image velocimetry (PIV) technique to capture the three-dimensional flow topology in a realistic and dynamic LV model. Secondly, using the 3D PIV technique as a gold standard to validate and develop promising medical imaging techniques: 4Dflow MRI and contrast enhanced 4D echo-PIV.
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11:00
15 mins
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A Simplified Atrial Electrogram Model for Tissue Conductivity Estimation
Bahareh Abdi, Richard C. Hendriks, Alle-Jan van der Veen, Natasja M.S. de Groot
Abstract: Gaining more understanding on the mechanism underlying atrial fibrillation depends partly on electrophysiological models. However, these models are typically rather complex, hampering further improvements. Signal processing based modelling and analysis can be a great help to bring such models to a higher abstraction level and easier estimate important underlying model parameters and, subsequently, better facilitate the diagnosis and treatment in a later stage.
In this work, we aim to estimate tissue conductivity from recorded electrograms as an indication of tissue (mal)functioning. To do so, we first develop a simple but effective forward model to replace the computationally intensive reaction-diffusion equations governing the electrical propagation in tissue. This parsimonious model opens up new possibilities for further processing of electrograms. Using the simplified model, we present a linear measurement model for electrograms based on conductivity. Subsequently, we exploit the simplicity of the linear model to solve the ill-posed inverse problem of estimating tissue conductivity.
As model validation, we present an initial algorithm for tissue conductivity estimation. The algorithm is demonstrated on simulated data as well as on clinically recorded data. The results show that the model allows to efficiently estimate the conductivity map. In addition, based on the estimated conductivity, realistic electrograms can be regenerated demonstrating the validity of the model.
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11:15
15 mins
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Myocardial Perfusion Phantom Experiments in CT
Marije E. Kamphuis, Gijs J. de Vries, Marcel J.W. Greuter, Riemer H.J.A. Slart, Cornelis H. Slump
Abstract: Myocardial perfusion imaging (MPI) is a common test method to diagnose obstructive coronary artery disease. Institutional variations like imaging modalities, vendors and protocols limit widespread implementation of a validated standard in MPI. In line with proper clinical decision-making, absolute quantification of myocardial blood flow (MBF) can facilitate validation and standardization. This research focuses on the development of a multimodal myocardial perfusion phantom, which we first tested in CT.
The prototype perfusion phantom comprises a cylindrical tube simulating the aorta (Ø=3 cm) and a dialysis filter (Fresenius) simulating the myocardium. Both are incorporated in an anthropomorphic thorax phantom (QRM GmbH). The simulated aorta and myocardium are built in separate open flow circuits to individually assess sensitivity to flow and contrast protocol variations. Each flow circuit has a gear pump that generates continuous aortic flow (3-6 Lmin-1) or myocardial flow (100-750 mLmin-1), respectively. Experiments were performed in contrast enhanced dynamic first-pass CT (SOMATOM, Force, Siemens). MPI was validated by comparing computed MBF from Perfusion CT software and measured flow (FCH-mini POM Flowmeter, Biotech) as reference. The MBF was computed according to the upslope method, whereby dividing the maximum slope of the myocardial time intensity curve (TIC) by the maximum intensity of the arterial input function (AIF).
Most of the requirements for the myocardial perfusion phantom, flow circuit and MBF quantification method were met. The obtained TICs from the phantom were compared to the TIC of healthy patients. Similar contrast enhancement was observed for the AIF, though the upslope myocardial perfusion rate was higher in the phantom setup compared to the in vivo situation. The computed and measured MBF showed a good correlation.
We have developed a novel, multimodal and multivendor myocardial perfusion phantom using measured sensor flow as reference. Initial results showed a good correlation between computed and measured MBF on CT.
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11:30
15 mins
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Variational Mode Decomposition Features for Heartbeat Classification
Amalia Villa Gomez, Sibasankar Padhy, Rik Willems, Sabine Van Huffel, Carolina Varon
Abstract: Aim: Heartbeat classification is required to detect abnormalities in the electrical activity of the heart and in applications such as the analysis of Heart Rate Variability. Currently commercially available software for heartbeat classification still requires extensive manual correction. Better algorithms for automatic heartbeat classification are therefore clinically valuable. In this study, Variational Mode Decomposition (VMD) is used to describe heartbeats regarding their origin, and their classification performance is evaluated combining those with time features. A single-lead approach is considered, aiming to use the features on wearable devices.
Methods: The features were evaluated on the first lead of the MIT-BIH Arrhythmia Database. Heartbeats were segmented using an asymmetric window of 0.65s, and the annotations were sorted in 3 groups following AAMI standards: Normal beats, Supra-ventricular and Ventricular extra-beats. Paced and fusion beats were not considered. Each heartbeat was decomposed in 5 modes, which correspond to the bands of frequency with the highest power. Five features were extracted from each of the bands: the power, the bandwidth, the symmetry of the wave around the R peak, the number of zero-crossings and the amplitude difference between the two lowest minima. Additionally, the distance to the previous and the next R peak, the absolute difference between them and the deviation of the previous RR interval from the average last 10 were used as time features. This set of features was fed to an LS-SVM classifier, using 10-fold cross-validation and 50% of the balanced data as training.
Results: Preliminary results report an overall accuracy of 92.14%. Regarding the performance for each class, for Normal, Supraventricular and Ventricular heartbeats we obtained a Sensitivity of 92.84%, 72.56% and 91.25% respectively; and a positive predictive value of 99.59%, 25.48% and 80.48%.
Conclusion: These results are in line with the state-of-the-art for heartbeat classification using single-lead, and they show promising performance for Normal and Ventricular beats. The discrimination between Normal and Supraventricular beats could be improved by including more sophisticated time features. Future work will focus on adding this kind of features, as well as on reducing the total number of features by applying a feature selection stage.
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11:45
15 mins
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Evaluation of Single Lead Ecg Features for Detecting Out-Of-Hospital Cardiac Arrests
Jordy Jansen, Richard Houben, Joël Karel, Ralf Peeters
Abstract: Sudden Cardiac Death (SCD) remains a vital health issue to this day and rapid EMS response is paramount for favourable neurological outcome [1]. Ventricular Fibrillation (VF) or Ventricular Tachycardia (VT) are the major causes of SCD. In order to detect VF and VT, many (relatively simple) single-lead ECG features have been proposed over the years that can be implemented in implantable or wearable devices. From the MIT-BIH Arrhythmia, VFDB and CUDB databases, a total of 37 of these features are extracted from electrocardiogram (ECG) segments, which were set to be 6, 12 or 18 seconds yielding three datasets. These ECGs act as a proxy for cardiograms. From a response time perspective, shorter segment lengths are preferable, but might not carry sufficient information. The research question is: what are the relevant features for VF/VT detection and what segment length ought to be used?
For these three data sets individually, a Support Vector Machine (SVM) and a Gradient Boosting Tree (GBT) model are trained on the features extracted from the training data (80%) and the parameters are optimized following a 5-fold cross validation technique. The SVM and GBT models are then evaluated on the test data (20%). The SVM and GBT models with all features fitted on the 6s dataset achieved the highest AUC, as well as specificity, accuracy and positive predictive value when sensitivity is fixed to 98% or 99%. Comparing the two models (on the 6s data set), the GBT model achieves highest AUC whereas the SVM model achieves higher specificity, accuracy and positive predictive value when sensitivity is fixed to 98% or 99%.
The information potential of the individual features was estimated by fitting an SVM for each feature individually. Regardless segment size, the three most informative features were time domain features, bCP [2], TCSC [3], count2 [4]. The results of this preliminary study are promising for robust VT/VF detection.
REFERENCES
[1] Wellens, Hein J, Lindemans, Fred W, Houben, Richard P, Gorgels, Anton P, Volders, Paul G, Ter Bekke, Rachel MA, and Crijns, Harry J. Improving survival after out-of-hospital cardiac arrest requires new tools. European Heart Journal, 37(19), 1499-1503, (2015).
[2] Irusta, Unai, Ruiz, Jesús, Aramendi, Elisabete, Gauna, Sofía Ruiz de, Ayala, Unai, and Alonso, Erik. A high-temporal resolution algorithm to discriminate shockable from non-shockable rhythms in adults and children. Resuscitation, 83(9), 1090-1097, (2012).
[3] Arafat, Muhammad Abdullah, Chowdhury, Abdul Wadud, and Hasan, Md Kamrul. A simple time domain algorithm for the detection of ventricular fibrillation in electrocardiogram. Signal, Image and Video Processing, 5(1), 1-10, (2011).
[4] Jekova, Irena and Krasteva, Vessela. Real time detection of ventricular fibrillation and tachycardia. Physiological measurement, 25(5), 1167-1178, (2004).
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