scholarly journals Basket-Type Catheters: Diagnostic Pitfalls Caused by Deformation and Limited Coverage

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Tobias Oesterlein ◽  
Daniel Frisch ◽  
Axel Loewe ◽  
Gunnar Seemann ◽  
Claus Schmitt ◽  
...  
Keyword(s):  
In Silico ◽  
Simulated Data ◽  
Electrode Arrangement ◽  
Catheter Design ◽  
Basket Catheter ◽  
Catheter Size ◽  
Mapping Techniques ◽  
Basket Model ◽  
Clinical Measurements ◽  
Diagnostic Pitfalls

Whole-chamber mapping using a 64-pole basket catheter (BC) has become a featured approach for the analysis of excitation patterns during atrial fibrillation. A flexible catheter design avoids perforation but may lead to spline bunching and influence coverage. We aim to quantify the catheter deformation and endocardial coverage in clinical situations and study the effect of catheter size and electrode arrangement using an in silico basket model. Atrial coverage and spline separation were evaluated quantitatively in an ensemble of clinical measurements. A computational model of the BC was implemented including an algorithm to adapt its shape to the atrial anatomy. Two clinically relevant mapping positions in each atrium were assessed in both clinical and simulated data. The simulation environment allowed varying both BC size and electrode arrangement. Results showed that interspline distances of more than 20 mm are common, leading to a coverage of less than 50% of the left atrial (LA) surface. In an ideal in silico scenario with variable catheter designs, a maximum coverage of 65% could be reached. As spline bunching and insufficient coverage can hardly be avoided, this has to be taken into account for interpretation of excitation patterns and development of new panoramic mapping techniques.

scholarly journals Technical advance in silico and in vitro development of a new bipolar radiofrequency ablation device for renal denervation

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Noel Pérez ◽  
Karl Muffly ◽  
Stephen E. Saddow
Keyword(s):  
Radiofrequency Ablation ◽  
Renal Artery ◽  
Resistant Hypertension ◽  
In Silico ◽  
Renal Denervation ◽  
Bipolar Electrode ◽  
Ablation Zone ◽  
Catheter Design ◽  
Basket Catheter

Abstract Background Renal denervation with radiofrequency ablation has become an accepted treatment for drug-resistant hypertension. However, there is a continuing need to develop new catheters for high-accuracy, targeted ablation. We therefore developed a radiofrequency bipolar electrode for controlled, targeted ablation through Joule heating induction between 60 and 100 °C. The bipolar design can easily be assembled into a basket catheter for deployment inside the renal artery. Methods Finite element modeling was used to determine the optimum catheter design to deliver a minimum ablation zone of 4 mm (W) × 10 mm (L) × 4 mm (H) within 60 s with a 500 kHz, 60 Vp-p signal, and 3 W maximum. The in silico model was validated with in vitro experiments using a thermochromic phantom tissue prepared with polyacrylamide gel and a thermochromic ink additive that permanently changes from pink to magenta when heated over 60 °C. Results The in vitro ablation zone closely matched the size and shape of the simulated area. The new electrode design directs the current density towards the artery walls and tissue, reducing unwanted blood temperature increases by focusing energy on the ablation zone. In contrast, the basket catheter design does not block renal flow during renal denervation. Conclusions This computational model of radiofrequency ablation can be used to estimate renal artery ablation zones for highly targeted renal denervation in patients with resistant hypertension. Furthermore, this innovative catheter has short ablation times and is one of the lowest power requirements of existing designs to perform the ablation.

scholarly journals Mapping dark matter and finding filaments: calibration of lensing analysis techniques on simulated data

2020 ◽  
Vol 496 (3) ◽  
pp. 3973-3990
Author(s):  
Sut-Ieng Tam ◽  
Richard Massey ◽  
Mathilde Jauzac ◽  
Andrew Robertson
Keyword(s):  
Gravitational Lensing ◽  
Simulated Data ◽  
Line Of Sight ◽  
Radial Density ◽  
Density Profiles ◽  
Analysis Techniques ◽  
Scientific Goal ◽  
Mass Mapping ◽  
Mapping Techniques ◽  
Suppress Noise

ABSTRACT We quantify the performance of mass mapping techniques on mock imaging and gravitational lensing data of galaxy clusters. The optimum method depends upon the scientific goal. We assess measurements of clusters’ radial density profiles, departures from sphericity, and their filamentary attachment to the cosmic web. We find that mass maps produced by direct (KS93) inversion of shear measurements are unbiased, and that their noise can be suppressed via filtering with mrlens. Forward-fitting techniques, such as lenstool, suppress noise further, but at a cost of biased ellipticity in the cluster core and overestimation of mass at large radii. Interestingly, current searches for filaments are noise-limited by the intrinsic shapes of weakly lensed galaxies, rather than by the projection of line-of-sight structures. Therefore, space-based or balloon-based imaging surveys that resolve a high density of lensed galaxies could soon detect one or two filaments around most clusters.

scholarly journals Rotor Localization and Phase Mapping of Cardiac Excitation Waves Using Deep Neural Networks

2021 ◽  
Vol 12 ◽  
Author(s):  
Jan Lebert ◽  
Namita Ravi ◽  
Flavio H. Fenton ◽  
Jan Christoph
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Optical Mapping ◽  
Simulated Data ◽  
Mapping Data ◽  
Phase Singularities ◽  
Rotor Core ◽  
Phase Mapping ◽  
Spatio Temporal ◽  
Mapping Techniques

The analysis of electrical impulse phenomena in cardiac muscle tissue is important for the diagnosis of heart rhythm disorders and other cardiac pathophysiology. Cardiac mapping techniques acquire local temporal measurements and combine them to visualize the spread of electrophysiological wave phenomena across the heart surface. However, low spatial resolution, sparse measurement locations, noise and other artifacts make it challenging to accurately visualize spatio-temporal activity. For instance, electro-anatomical catheter mapping is severely limited by the sparsity of the measurements, and optical mapping is prone to noise and motion artifacts. In the past, several approaches have been proposed to create more reliable maps from noisy or sparse mapping data. Here, we demonstrate that deep learning can be used to compute phase maps and detect phase singularities in optical mapping videos of ventricular fibrillation, as well as in very noisy, low-resolution and extremely sparse simulated data of reentrant wave chaos mimicking catheter mapping data. The self-supervised deep learning approach is fundamentally different from classical phase mapping techniques. Rather than encoding a phase signal from time-series data, a deep neural network instead learns to directly associate phase maps and the positions of phase singularities with short spatio-temporal sequences of electrical data. We tested several neural network architectures, based on a convolutional neural network (CNN) with an encoding and decoding structure, to predict phase maps or rotor core positions either directly or indirectly via the prediction of phase maps and a subsequent classical calculation of phase singularities. Predictions can be performed across different data, with models being trained on one species and then successfully applied to another, or being trained solely on simulated data and then applied to experimental data. Neural networks provide a promising alternative to conventional phase mapping and rotor core localization methods. Future uses may include the analysis of optical mapping studies in basic cardiovascular research, as well as the mapping of atrial fibrillation in the clinical setting.

scholarly journals Directed graph mapping is able to distinguish between the true and false rotors in a complex in-silico excitation pattern

EP Europace ◽  
2021 ◽  
Vol 23 (Supplement_3) ◽  
Author(s):  
E Van Nieuwenhuyse ◽  
L Martinez-Mateu ◽  
J Saiz ◽  
A V Panfilov ◽  
N Vandersickel
Keyword(s):  
Directed Graph ◽  
In Silico ◽  
Directed Network ◽  
Far Field ◽  
Field Effects ◽  
In Silico Studies ◽  
Basket Catheter ◽  
Graph Mapping ◽  
Excitation Pattern ◽  
Public Grant

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – EU funding. Main funding source(s): Supported in part by Dirección General de Polı́tica Cientı́fica de la Generalitat Valenciana PROMETEU 2020/043 Background In realistic in-silico studies (Figure1, top row) it was shown that phase mapping PM (Figure 1, A) can detect the correct rotor as well as phantom rotors as an artefact of interpolation or due to the far field (Figure 1, B). After interpretation of the LAT, the far field detections could not be distinguished from the true rotor driving the excitation pattern. This can contribute to failure in Atrial Fibrillation (AF) ablation procedures. Objective We tested if the recently developed tool Directed Graph mapping (DGM) is less prone to far-field effects and interpolation artefacts than PM on the same in-silico data. DGM represents the excitation pattern as a directed network, from which the rotational activity is detected as cycles in that network. Methods Starting from the electrograms (EGMs) of the 64 electrode basket catheter, we interpolated to 957 equidistant electrodes and calculated local activation times (LATs) of the interpolated EGMs (Figure 1, C). We varied the minimal allowed conduction velocity and calculated the corresponding networks for the complete simulation time. Detections were considered as correct if they were located in the same region of the true core of the phasemaps. The false detections were classified in multiple different regions (Figure 1, D). Results We find that by proper choice of CVs in the graphs it is possible to achieve a 80% detection of true rotors with 26% detection of false rotors. Reducing restrictions on the CVs increased the detection rate of the false rotors. False rotors due to artifacts were not detected by DGM (Figure 1, last row). Conclusion DGM is able to distinguish between true and far field rotors. False detections due to interpolation artifacts as seen in the PM protocol were not observed. The velocity limits in the graph construction play a keyrole in eliminating the far field effects. Abstract Figure 1

scholarly journals In Silico and in Vitro Development of a New Bipolar Radiofrequency Ablation Device for Renal Denervation

2021 ◽  
Author(s):  
Noel Pérez ◽  
Karl Muffly ◽  
Stephen E. Saddow
Keyword(s):  
Radiofrequency Ablation ◽  
Computational Model ◽  
Resistant Hypertension ◽  
Renal Denervation ◽  
Ablation Zone ◽  
Power Requirement ◽  
Bipolar Electrodes ◽  
Catheter Design ◽  
Basket Catheter

Abstract Background: Renal denervation with radiofrequency ablation has become an accepted treatment for drug-resistant hypertension. However, there is a continuing need to develop new catheters for high-accuracy, targeted ablation. We, therefore, developed a radiofrequency ablation device with a basket catheter and bipolar electrodes for controlled, targeted ablation through Joule heating induction between 60°C and 100°C.Methods: Finite element modeling was used to determine the optimum catheter design to deliver a minimum ablation zone of 4 mm (W) x 10 mm (L) x 4 mm (H) within 60 seconds with a 500 kHz, 60 Vp-p signal, and 0.9 W maximum. The computational model was validated using in vitro phantom tissue impregnated with a color-changing thermochromic pigment.Results: The in vitro ablation zone closely matched the size and shape of the simulated area. The new electrode design directs the current density towards the artery walls and tissue, reducing unwanted blood temperature increases by focusing energy on the ablation zone. In contrast, the basket catheter design does not block renal flow during renal denervation.Conclusions: This computational model of radiofrequency ablation can be used to estimate renal artery ablation zones for highly targeted renal denervation in patients with resistant hypertension. Furthermore, this innovative catheter has short ablation times and the lowest power requirement of existing designs to perform the ablation.

scholarly journals M&Ms: A software for building realistic Microbial Mock communities

2021 ◽  
Author(s):  
Natalia García-García ◽  
Javier Tamames ◽  
Fernando Puente-Sánchez
Keyword(s):  
Microbial Communities ◽  
In Silico ◽  
Source Code ◽  
Simulated Data ◽  
Bioinformatic Tools ◽  
Sequencing Technologies ◽  
Bioinformatic Tool ◽  
Reference Sequences ◽  
User Friendly ◽  
Mock Communities

Motivation: Advances in sequencing technologies have triggered the development of many bioinformatic tools aimed to analyze these data. As these tools need to be tested, it is important to simulate datasets that resemble realistic conditions. Although there is a large amount of software dedicated to produce reads from in silico microbial communities, often the simulated data diverge widely from real situations. Results: Here, we introduce M&Ms, a user-friendly open-source bioinformatic tool to produce realistic amplicon datasets from reference sequences, based on pragmatic ecological parameters. This tool creates sequence libraries for in silico microbial communities with user-controlled richness, evenness, microdiversity, and source environment. M&Ms allows the user to generate simple to complex read datasets based on real parameters that can be used in developing bioinformatic software or in benchmarking current tools. M&Ms also provides additional figures and files with extensive details on how each synthetic community is composed, so that users can make informed choices when designing their benchmarking pipelines. Availability: The source code of M&Ms is freely available from https://github.com/ggnatalia/MMs

Combining field potential data and in silico simulated data to improve machine learning approaches and better assess drug cardiotoxicity

2020 ◽  
Vol 105 ◽  
pp. 106792
Author(s):  
Fabien Raphel ◽  
Tessa de Korte ◽  
Damiano Lombardi ◽  
Stefan Braam ◽  
Christophe Bleunven ◽  
...  
Keyword(s):  
Machine Learning ◽  
In Silico ◽  
Field Potential ◽  
Simulated Data ◽  
Learning Approaches

scholarly journals Causes of altered ventricular mechanics in hypertrophic cardiomyopathy: an in-silico study

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Ekaterina Kovacheva ◽  
Tobias Gerach ◽  
Steffen Schuler ◽  
Marco Ochs ◽  
Olaf Dössel ◽  
...  
Keyword(s):  
Hypertrophic Cardiomyopathy ◽  
In Silico ◽  
Numerical Study ◽  
Radial Strain ◽  
Sensitivity Study ◽  
Homogeneous Distribution ◽  
Wall Thickening ◽  
Tissue Properties ◽  
Ventricular Mechanics ◽  
Clinical Measurements

Abstract Background Hypertrophic cardiomyopathy (HCM) is typically caused by mutations in sarcomeric genes leading to cardiomyocyte disarray, replacement fibrosis, impaired contractility, and elevated filling pressures. These varying tissue properties are associated with certain strain patterns that may allow to establish a diagnosis by means of non-invasive imaging without the necessity of harmful myocardial biopsies or contrast agent application. With a numerical study, we aim to answer: how the variability in each of these mechanisms contributes to altered mechanics of the left ventricle (LV) and if the deformation obtained in in-silico experiments is comparable to values reported from clinical measurements. Methods We conducted an in-silico sensitivity study on physiological and pathological mechanisms potentially underlying the clinical HCM phenotype. The deformation of the four-chamber heart models was simulated using a finite-element mechanical solver with a sliding boundary condition to mimic the tissue surrounding the heart. Furthermore, a closed-loop circulatory model delivered the pressure values acting on the endocardium. Deformation measures and mechanical behavior of the heart models were evaluated globally and regionally. Results Hypertrophy of the LV affected the course of strain, strain rate, and wall thickening—the root-mean-squared difference of the wall thickening between control (mean thickness 10 mm) and hypertrophic geometries (17 mm) was >10%. A reduction of active force development by 40% led to less overall deformation: maximal radial strain reduced from 26 to 21%. A fivefold increase in tissue stiffness caused a more homogeneous distribution of the strain values among 17 heart segments. Fiber disarray led to minor changes in the circumferential and radial strain. A combination of pathological mechanisms led to reduced and slower deformation of the LV and halved the longitudinal shortening of the LA. Conclusions This study uses a computer model to determine the changes in LV deformation caused by pathological mechanisms that are presumed to underlay HCM. This knowledge can complement imaging-derived information to obtain a more accurate diagnosis of HCM.

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