Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.
Deep brain stimulation
BrainStimulator is a set of networks that are used in SCIRun to perform simulations of brain stimulation such as transcranial direct current stimulation (tDCS) and magnetic transcranial stimulation (TMS).
Developing software tools for science has always been a central vision of the SCI Institute.

SCI Publications

2022


J. A. Bergquist, J. Coll-Font, B. Zenger, L. C. Rupp, W. W. Good, D. H. Brooks, R. S. MacLeod. “Reconstruction of cardiac position using body surface potentials,” In Computers in Biology and Medicine, Vol. 142, pp. 105174. 2022.
DOI: https://doi.org/10.1016/j.compbiomed.2021.105174

ABSTRACT

Electrocardiographic imaging (ECGI) is a noninvasive technique to assess the bioelectric activity of the heart which has been applied to aid in clinical diagnosis and management of cardiac dysfunction. ECGI is built on mathematical models that take into account several patient specific factors including the position of the heart within the torso. Errors in the localization of the heart within the torso, as might arise due to natural changes in heart position from respiration or changes in body position, contribute to errors in ECGI reconstructions of the cardiac activity, thereby reducing the clinical utility of ECGI. In this study we present a novel method for the reconstruction of cardiac geometry utilizing noninvasively acquired body surface potential measurements. Our geometric correction method simultaneously estimates the cardiac position over a series of heartbeats by leveraging an iterative approach which alternates between estimating the cardiac bioelectric source across all heartbeats and then estimating cardiac positions for each heartbeat. We demonstrate that our geometric correction method is able to reduce geometric error and improve ECGI accuracy in a wide range of testing scenarios. We examine the performance of our geometric correction method using different activation sequences, ranges of cardiac motion, and body surface electrode configurations. We find that after geometric correction resulting ECGI solution accuracy is improved and variability of the ECGI solutions between heartbeats is substantially reduced.



Y. Ishidoya, E. Kwan, D. J. Dosdall, R. S. Macleod, L. Navaravong, B. A. Steinberg, T. J. Bunch, R. Ranjan. “Short-Term Natural Course of Esophageal Thermal Injury After Ablation for Atrial Fibrillation,” In Journal of Cardiovascular Electrophysiology, Wiley, 2022.
DOI: 10.1111/jce.15553

ABSTRACT

Purpose
To provide insight into the short-term natural history of esophageal thermal injury (ETI) after radiofrequency catheter ablation (RFCA) for atrial fibrillation (AF) by esophagogastroduodenoscopy (EGD).

Methods
We screened patients who underwent RFCA for AF and EGD based on esophageal late gadolinium enhancement (LGE) in post ablation MRI. Patients with ETI diagnosed with EGD were included. We defined severity of ETI according to Kansas City classification (KCC): type 1: erythema; type 2: ulcers (2a: superficial; 2b deep); type 3 perforation (3a: perforation; 3b: perforation with atrioesophageal fistula). Repeated EGD was performed within 1-14 days after the last EGD if recommended and possible until any certain healing signs (visible reduction in size without deepening of ETI or complete resolution) were observed.
Results
ETI was observed in 62 of 378 patients who underwent EGD after RFCA. Out of these 62 patients with ETI, 21% (13) were type 1, 50% (31) were type 2a and 29% (18) were type 2b at the initial EGD. All esophageal lesions, but one type 2b lesion that developed into an atrioesophageal fistula (AEF), showed signs of healing in repeated EGD studies within 14 days after the procedure. The one type 2b lesion developing into an AEF showed an increase in size and ulcer deepening in repeat EGD 8 days after the procedure.
Conclusion
We found that all ETI which didn't progress to AEF presented healing signs within 14 days after the procedure and that worsening ETI might be an early signal for developing esophageal perforation.



Y. Ishidoya, E. Kwan, D. J. Dosdall, R. S. Macleod, L. Navaravong, B. A. Steinberg, T. J. Bunch, R. Ranjan. “Shorter Distance Between The Esophagus And The Left Atrium Is Associated With Higher Rates Of Esophageal Thermal Injury After Radiofrequency Ablation,” In Journal of Cardiovascular Electrophysiology, Wiley, 2022.
DOI: 10.1111/jce.15554

ABSTRACT

Background
Esophageal thermal injury (ETI) is a known and potentially serious complication of catheter ablation for atrial fibrillation. We intended to evaluate the distance between the esophagus and the left atrium posterior wall (LAPW) and its association with esophageal thermal injury.

Methods
A retrospective analysis of 73 patients who underwent esophagogastroduodenoscopy (EGD) after LA radiofrequency catheter ablation for symptomatic atrial fibrillation and pre-ablation magnetic resonance imaging (MRI) was used to identify the minimum distance between the inner lumen of the esophagus and the ablated atrial endocardium (pre-ablation atrial esophageal distance; pre-AED) and occurrence of ETI. Parameters of ablation index (AI, Visitag Surpoint) were collected in 30 patients from the CARTO3 system and compared to assess if ablation strategies and AI further impacted risk of ETI.
Results
Pre-AED was significantly larger in patients without ETI than those with ETI (5.23 ± 0.96 mm vs 4.31 ± 0.75 mm, p < 0.001). Pre-AED showed high accuracy for predicting ETI with the best cutoff value of 4.37 mm. AI was statistically comparable between Visitag lesion markers with and without associated esophageal late gadolinium enhancement (LGE) detected by post-ablation MRI in the low-power long-duration ablation group (LPLD, 25-40W for 10 to 30 s, 393.16 [308.62, 408.86] versus 406.58 [364.38, 451.22], p = 0.16) and high-power short-duration group (HPSD, 50W for 5-10 s, 336.14 [299.66, 380.11] versus 330.54 [286.21, 384.71], p = 0.53), respectively.
Conclusion
Measuring the distance between the LA and the esophagus in pre-ablation LGE-MRI could be helpful in predicting ETI after LAPW ablation.



X. Jiang, Z. Li, R. Missel, Md. Zaman, B. Zenger, W. W. Good, R. S. MacLeod, J. L. Sapp, L. Wang. “Few-Shot Generation of Personalized Neural Surrogates for Cardiac Simulation via Bayesian Meta-learning,” In Medical Image Computing and Computer Assisted Intervention -- MICCAI 2022, Springer Nature Switzerland, pp. 46--56. 2022.
ISBN: 978-3-031-16452-1
DOI: 10.1007/978-3-031-16452-1_5

ABSTRACT

Clinical adoption of personalized virtual heart simulations faces challenges in model personalization and expensive computation. While an ideal solution is an efficient neural surrogate that at the same time is personalized to an individual subject, the state-of-the-art is either concerned with personalizing an expensive simulation model, or learning an efficient yet generic surrogate. This paper presents a completely new concept to achieve personalized neural surrogates in a single coherent framework of meta-learning (metaPNS). Instead of learning a single neural surrogate, we pursue the process of learning a personalized neural surrogate using a small amount of context data from a subject, in a novel formulation of few-shot generative modeling underpinned by: 1) a set-conditioned neural surrogate for cardiac simulation that, conditioned on subject-specific context data, learns to generate query simulations not included in the context set, and 2) a meta-model of amortized variational inference that learns to condition the neural surrogate via simple feed-forward embedding of context data. As test time, metaPNS delivers a personalized neural surrogate by fast feed-forward embedding of a small and flexible number of data available from an individual, achieving -- for the first time -- personalization and surrogate construction for expensive simulations in one end-to-end learning framework. Synthetic and real-data experiments demonstrated that metaPNS was able to improve personalization and predictive accuracy in comparison to conventionally-optimized cardiac simulation models, at a fraction of computation.



R. Kamali, K. Gillete, J. Tate, D. A. Abhyankar, D. J. Dosdall, G. Plank, T. J. Bunch, R. S. Macleod & R. Ranjan . “Treatment Planning for Atrial Fibrillation Using Patient-Specific Models Showing the Importance of Fibrillatory-Areas,” In Annals of Biomedical Engineering, Springer, 2022.
DOI: https://doi.org/10.1007/s10439-022-03029-5

ABSTRACT

Computational models have made it possible to study the effect of fibrosis and scar on atrial fibrillation (AF) and plan future personalized treatments. Here, we study the effect of area available for fibrillatory waves to sustain AF. Then we use it to plan for AF ablation to improve procedural outcomes. CARPentry was used to create patient-specific models to determine the association between the size of residual contiguous areas available for AF wavefronts to propagate and sustain AF [fibrillatory area (FA)] after ablation with procedural outcomes. The FA was quantified in a novel manner accounting for gaps in ablation lines. We selected 30 persistent AF patients with known ablation outcomes. We divided the atrial surface into five areas based on ablation scar pattern and anatomical landmarks and calculated the FAs. We validated the models based on clinical outcomes and suggested future ablation lines that minimize the FAs and terminate rotor activities in simulations. We also simulated the effects of three common antiarrhythmic drugs. In the patient-specific models, the predicted arrhythmias matched the clinical outcomes in 25 of 30 patients (accuracy 83.33%). The average largest FA (FAmax) in the recurrence group was 8517 ± 1444 vs. 6772 ± 1531 mm2 in the no recurrence group (p < 0.004). The final FAs after adding the suggested ablation lines in the AF recurrence group reduced the average FAmax from 8517 ± 1444 to 6168 ± 1358 mm2 (p < 0.001) and stopped the sustained rotor activity. Simulations also correctly anticipated the effect of antiarrhythmic drugs in 5 out of 6 patients who used drug therapy post unsuccessful ablation (accuracy 83.33%). Sizes of FAs available for AF wavefronts to propagate are important determinants for ablation outcomes. FA size in combination with computational simulations can be used to direct ablation in persistent AF to minimize the critical mass required to sustain recurrent AF.



M. Toloubidokhti, N. Kumar, Z. Li, P. K. Gyawali, B. Zenger, W. W. Good, R. S. MacLeod, L. Wang . “Interpretable Modeling and Reduction of Unknown Errors in Mechanistic Operators,” In Medical Image Computing and Computer Assisted Intervention -- MICCAI 2022, Springer Nature Switzerland, pp. 459--468. 2022.
ISBN: 978-3-031-16452-1
DOI: 10.1007/978-3-031-16452-1_44

ABSTRACT

Prior knowledge about the imaging physics provides a mechanistic forward operator that plays an important role in image reconstruction, although myriad sources of possible errors in the operator could negatively impact the reconstruction solutions. In this work, we propose to embed the traditional mechanistic forward operator inside a neural function, and focus on modeling and correcting its unknown errors in an interpretable manner. This is achieved by a conditional generative model that transforms a given mechanistic operator with unknown errors, arising from a latent space of self-organizing clusters of potential sources of error generation. Once learned, the generative model can be used in place of a fixed forward operator in any traditional optimization-based reconstruction process where, together with the inverse solution, the error in prior mechanistic forward operator can be minimized and the potential source of error uncovered. We apply the presented method to the reconstruction of heart electrical potential from body surface potential. In controlled simulation experiments and in-vivo real data experiments, we demonstrate that the presented method allowed reduction of errors in the physics-based forward operator and thereby delivered inverse reconstruction of heart-surface potential with increased accuracy.


2021


J. A. Bergquist, W. W. Good, B. Zenger, J. D. Tate, L. C. Rupp, R. S. MacLeod. “The Electrocardiographic Forward Problem: A Benchmark Study,” In Computers in Biology and Medicine, Vol. 134, Pergamon, pp. 104476. 2021.
DOI: https://doi.org/10.1016/j.compbiomed.2021.104476

ABSTRACT

Background
Electrocardiographic forward problems are crucial components for noninvasive electrocardiographic imaging (ECGI) that compute torso potentials from cardiac source measurements. Forward problems have few sources of error as they are physically well posed and supported by mature numerical and computational techniques. However, the residual errors reported from experimental validation studies between forward computed and measured torso signals remain surprisingly high.

Objective
To test the hypothesis that incomplete cardiac source sampling, especially above the atrioventricular (AV) plane is a major contributor to forward solution errors.

Methods
We used a modified Langendorff preparation suspended in a human-shaped electrolytic torso-tank and a novel pericardiac-cage recording array to thoroughly sample the cardiac potentials. With this carefully controlled experimental preparation, we minimized possible sources of error, including geometric error and torso inhomogeneities. We progressively removed recorded signals from above the atrioventricular plane to determine how the forward-computed torso-tank potentials were affected by incomplete source sampling.

Results
We studied 240 beats total recorded from three different activation sequence types (sinus, and posterior and anterior left-ventricular free-wall pacing) in each of two experiments. With complete sampling by the cage electrodes, all correlation metrics between computed and measured torso-tank potentials were above 0.93 (maximum 0.99). The mean root-mean-squared error across all beat types was also low, less than or equal to 0.10 mV. A precipitous drop in forward solution accuracy was observed when we included only cage measurements below the AV plane.

Conclusion
First, our forward computed potentials using complete cardiac source measurements set a benchmark for similar studies. Second, this study validates the importance of complete cardiac source sampling above the AV plane to produce accurate forward computed torso potentials. Testing ECGI systems and techniques with these more complete and highly accurate datasets will improve inverse techniques and noninvasive detection of cardiac electrical abnormalities.



W. W. Good, B. Zenger, J. A. Bergquist, L. C. Rupp, K. K. Gillette, M. A.F. Gsell, G. Plank, R. S. MacLeod. “Quantifying the spatiotemporal influence of acute myocardial ischemia on volumetric conduction velocity,” In Journal of Electrocardiology, Vol. 66, Churchill Livingstone, pp. 86-94. 2021.

ABSTRACT

Introduction
Acute myocardial ischemia occurs when coronary perfusion to the heart is inadequate, which can perturb the highly organized electrical activation of the heart and can result in adverse cardiac events including sudden cardiac death. Ischemia is known to influence the ST and repolarization phases of the ECG, but it also has a marked effect on propagation (QRS); however, studies investigating propagation during ischemia have been limited.

Methods
We estimated conduction velocity (CV) and ischemic stress prior to and throughout 20 episodes of experimentally induced ischemia in order to quantify the progression and correlation of volumetric conduction changes during ischemia. To estimate volumetric CV, we 1) reconstructed the activation wavefront; 2) calculated the elementwise gradient to approximate propagation direction; and 3) estimated conduction speed (CS) with an inverse-gradient technique.
Results
We found that acute ischemia induces significant conduction slowing, reducing the global median speed by 20 cm/s. We observed a biphasic response in CS (acceleration then deceleration) early in some ischemic episodes. Furthermore, we noted a high temporal correlation between ST-segment changes and CS slowing; however, when comparing these changes over space, we found only moderate correlation (corr. = 0.60).
Discussion
This study is the first to report volumetric CS changes (acceleration and slowing) during episodes of acute ischemia in the whole heart. We showed that while CS changes progress in a similar time course to ischemic stress (measured by ST-segment shifts), the spatial overlap is complex and variable, showing extreme conduction slowing both in and around regions experiencing severe ischemia.



W. W. Good, K. Gillette, B. Zenger, J. Bergquist, L. C. Rupp, J. D. Tate, D. Anderson, M. Gsell, G. Plank, R. S. Macleod. “Estimation and validation of cardiac conduction velocity and wavefront reconstruction using epicardial and volumetric data,” In IEEE Transactions on Biomedical Engineering, IEEE, 2021.
DOI: 10.1109/TBME.2021.3069792

ABSTRACT

Objective: In this study, we have used whole heart simulations parameterized with large animal experiments to validate three techniques (two from the literature and one novel) for estimating epicardial and volumetric conduction velocity (CV). Methods: We used an eikonal-based simulation model to generate ground truth activation sequences with prescribed CVs. Using the sampling density achieved experimentally we examined the accuracy with which we could reconstruct the wavefront, and then examined the robustness of three CV estimation techniques to reconstruction related error. We examined a triangulation-based, inverse-gradient-based, and streamline-based techniques for estimating CV cross the surface and within the volume of the heart. Results: The reconstructed activation times agreed closely with simulated values, with 50-70% of the volumetric nodes and 97-99% of the epicardial nodes were within 1 ms of the ground truth. We found close agreement between the CVs calculated using reconstructed versus ground truth activation times, with differences in the median estimated CV on the order of 3-5% volumetrically and 1-2% superficially, regardless of what technique was used. Conclusion: Our results indicate that the wavefront reconstruction and CV estimation techniques are accurate, allowing us to examine changes in propagation induced by experimental interventions such as acute ischemia, ectopic pacing, or drugs. Significance: We implemented, validated, and compared the performance of a number of CV estimation techniques. The CV estimation techniques implemented in this study produce accurate, high-resolution CV fields that can be used to study propagation in the heart experimentally and clinically.



W. W. Good, B. Zenger, J. A. Bergquist, L. C. Rupp, K. Gillett, N. Angel, D. Chou, G. Plank, R. S. MacLeod. “Combining endocardial mapping and electrocardiographic imaging (ECGI) for improving PVC localization: A feasibility study,” In Journal of Electrocardiology, 2021.
ISSN: 0022-0736
DOI: https://doi.org/10.1016/j.jelectrocard.2021.08.013

ABSTRACT

Introduction

Accurate reconstruction of cardiac activation wavefronts is crucial for clinical diagnosis, management, and treatment of cardiac arrhythmias. Furthermore, reconstruction of activation profiles within the intramural myocardium has long been impossible because electrical mapping was only performed on the endocardial surface. Recent advancements in electrocardiographic imaging (ECGI) have made endocardial and epicardial activation mapping possible. We propose a novel approach to use both endocardial and epicardial mapping in a combined approach to reconstruct intramural activation times.

Objective

To implement and validate a combined epicardial/endocardial intramural activation time reconstruction technique.
Methods

We used 11 simulations of ventricular activation paced from sites throughout myocardial wall and extracted endocardial and epicardial activation maps at approximate clinical resolution. From these maps, we interpolated the activation times through the myocardium using thin-plate-spline radial basis functions. We evaluated activation time reconstruction accuracy using root-mean-squared error (RMSE) of activation times and the percent of nodes within 1 ms of the ground truth.
Results

Reconstructed intramural activation times showed an RMSE and percentage of nodes within 1 ms of the ground truth simulations of 3 ms and 70%, respectively. In the worst case, the RMSE and percentage of nodes were 4 ms and 60%, respectively.
Conclusion

We showed that a simple, yet effective combination of clinical endocardial and epicardial activation maps can accurately reconstruct intramural wavefronts. Furthermore, we showed that this approach provided robust reconstructions across multiple intramural stimulation sites.



X. Jiang, J. C. Font, J. A. Bergquist, B. Zenger, W. W. Good, D. H. Brooks, R. S. MacLeod, L. Wang. “Deep Adaptive Electrocardiographic Imaging with Generative Forward Model for Error Reduction,” In Functional Imaging and Modeling of the Heart: 11th International Conference, In Functional Imaging and Modeling of the Heart: 11th International Conference, Vol. 12738, Springer Nature, pp. 471. 2021.

ABSTRACT

Accuracy of estimating the heart’s electrical activity with Electrocardiographic Imaging (ECGI) is challenging due to using an error-prone physics-based model (forward model). While getting better results than the traditional numerical methods following the underlying physics, modern deep learning approaches ignore the physics behind the electrical propagation in the body and do not allow the use of patientspecific geometry. We introduce a deep-learning-based ECGI framework capable of understanding the underlying physics, aware of geometry, and adjustable to patient-specific data. Using a variational autoencoder (VAE), we uncover the forward model’s parameter space, and when solving the inverse problem, these parameters will be optimized to reduce the errors in the forward model. In both simulation and real data experiments, we demonstrated the ability of the presented framework to provide accurate reconstruction of the heart’s electrical potentials and localization of the earliest activation sites.



R. Kamali, J. Kump, E. Ghafoori, M. Lange, N. Hu, T. J. Bunch, D. J. Dosdall, R. S. Macleod, R. Ranjan. “Area Available for Atrial Fibrillation to Propagate Is an Important Determinant of Recurrence After Ablation,” In JACC: Clinical Electrophysiology, Elsevier, 2021.

ABSTRACT

This study sought to evaluate atrial fibrillation (AF) ablation outcomes based on scar patterns and contiguous area available for AF wavefronts to propagate.



J. D. Tate, W. W. Good, N. Zemzemi, M. Boonstra, P. van Dam, D. H. Brooks, A. Narayan, R. S. MacLeod. “Uncertainty Quantification of the Effects of Segmentation Variability in ECGI,” In Functional Imaging and Modeling of the Heart, Springer International Publishing, pp. 515--522. 2021.
DOI: 10.1007/978-3-030-78710-3_49

ABSTRACT

Despite advances in many of the techniques used in Electrocardiographic Imaging (ECGI), uncertainty remains insufficiently quantified for many aspects of the pipeline. The effect of geometric uncertainty, particularly due to segmentation variability, may be the least explored to date. We use statistical shape modeling and uncertainty quantification (UQ) to compute the effect of segmentation variability on ECGI solutions. The shape model was made with Shapeworks from nine segmentations of the same patient and incorporated into an ECGI pipeline. We computed uncertainty of the pericardial potentials and local activation times (LATs) using polynomial chaos expansion (PCE) implemented in UncertainSCI. Uncertainty in pericardial potentials from segmentation variation mirrored areas of high variability in the shape model, near the base of the heart and the right ventricular outflow tract, and that ECGI was less sensitive to uncertainty in the posterior region of the heart. Subsequently LAT calculations could vary dramatically due to segmentation variability, with a standard deviation as high as 126ms, yet mainly in regions with low conduction velocity. Our shape modeling and UQ pipeline presented possible uncertainty in ECGI due to segmentation variability and can be used by researchers to reduce said uncertainty or mitigate its effects. The demonstrated use of statistical shape modeling and UQ can also be extended to other types of modeling pipelines.



B. Zenger, W. W. Good, J. A. Bergquist, L. C. Rupp, M. Perez, G. J. Stoddard, V. Sharma, R. S. MacLeod. “Transient recovery of epicardial and torso ST-segment ischemic signals during cardiac stress tests: A possible physiological mechanism,” In Journal of Electrocardiology, Churchill Livingstone, 2021.

ABSTRACT

Background

Acute myocardial ischemia has several characteristic ECG findings, including clinically detectable ST-segment deviations. However, the sensitivity and specificity of diagnosis based on ST-segment changes are low. Furthermore, ST-segment deviations have been shown to be transient and spontaneously recover without any indication the ischemic event has subsided.

Objective

Assess the transient recovery of ST-segment deviations on remote recording electrodes during a partial occlusion cardiac stress test and compare them to intramyocardial ST-segment deviations.

Methods

We used a previously validated porcineBZ experimental model of acute myocardial ischemia with controllable ischemic load and simultaneous electrical measurements within the heart wall, on the epicardial surface, and on the torso surface. Simulated cardiac stress tests were induced by occluding a coronary artery while simultaneously pacing rapidly or infusing dobutamine to stimulate cardiac function. Postexperimental imaging created anatomical models for data visualization and quantification. Markers of ischemia were identified as deviations in the potentials measured at 40% of the ST-segment. Intramural cardiac conduction speed was also determined using the inverse gradient method. We assessed changes in intramyocardial ischemic volume proportion, conduction speed, clinical presence of ischemia on remote recording arrays, and regional changes to intramyocardial ischemia. We defined the peak deviation response time as the time interval after onset of ischemia at which maximum ST-segment deviation was achieved, and ST-recovery time was the interval when ST deviation returned to below thresholded of ST elevation.

Results

In both epicardial and torso recordings, the peak ST-segment deviation response time was 4.9±1.1 min and the ST-recovery time was approximately 7.9±2.5 min, both well before the termination of the ischemic stress. At peak response time, conduction speed was reduced by 50% and returned to near baseline at ST-recovery. The overall ischemic volume proportion initially increased, on average, to 37% at peak response time; however, it recovered only to 30% at the ST-recovery time. By contrast, the subepicardial region of the myocardial wall showed 40% ischemic volume at peak response time and recovered much more strongly to 25% as epicardial ST-segment deviations returned to baseline.

Conclusion

Our data show that remote ischemic signal recovery correlates with a recovery of the subepicardial myocardium, while subendocardial ischemic development persists.


2019


A. Prakosa, H.J. Arevalo, D. Deng, P.M. Boyle, P.P. Nikolov, H. Ashikaga, J.E. Blauer, E. Ghafoori, C.J. Park, R.C. Blake III, F.T. Han, R.S. MacLeod, H.R. Halperin, D.J. Callans, R. Ranjan, J. Chrispin, S. Nazarian,, N.A. Trayanova. “Personalized Virtual-heart Technology for Guiding the Ablation of Infarct-related Ventricular Tachycardia,” In Nature Biomedical Engineering, Vol. 2, pp. 732–740. 2019.
DOI: doi.org/10.1038/s41551-018-0282-2

ABSTRACT

Ventricular tachycardia (VT), which can lead to sudden cardiac death, occurs frequently in patients with myocardial infarction. Catheter-based radio-frequency ablation of cardiac tissue has achieved only modest efficacy, owing to the inaccurate identification of ablation targets by current electrical mapping techniques, which can lead to extensive lesions and to a prolonged, poorly tolerated procedure. Here, we show that personalized virtual-heart technology based on cardiac imaging and computational modelling can identify optimal infarct-related VT ablation targets in retrospective animal (five swine) and human studies (21 patients), as well as in a prospective feasibility study (five patients). We first assessed, using retrospective studies (one of which included a proportion of clinical images with artefacts), the capability of the technology to determine the minimum-size ablation targets for eradicating all VTs. In the prospective study, VT sites predicted by the technology were targeted directly, without relying on prior electrical mapping. The approach could improve infarct-related VT ablation guidance, where accurate identification of patient-specific optimal targets could be achieved on a personalized virtual heart before the clinical procedure.


2018


B.M. Burton, K.K. Aras, W.W. Good, J.D. Tate, B. Zenger, R.S. MacLeod. “A Framework for Image-Based Modeling of Acute Myocardial Ischemia Using Intramurally Recorded Extracellular Potentials,” In Annals of Biomedical Engineering, Springer Nature, May, 2018.
DOI: 10.1007/s10439-018-2048-0

ABSTRACT

The biophysical basis for electrocardiographic evaluation of myocardial ischemia stems from the notion that ischemic tissues develop, with relative uniformity, along the endocardial aspects of the heart. These injured regions of subendocardial tissue give rise to intramural currents that lead to ST segment deflections within electrocardiogram (ECG) recordings. The concept of subendocardial ischemic regions is often used in clinical practice, providing a simple and intuitive description of ischemic injury; however, such a model grossly oversimplifies the presentation of ischemic disease—inadvertently leading to errors in ECG-based diagnoses. Furthermore, recent experimental studies have brought into question the subendocardial ischemia paradigm suggesting instead a more distributed pattern of tissue injury. These findings come from experiments and so have both the impact and the limitations of measurements from living organisms. Computer models have often been employed to overcome the constraints of experimental approaches and have a robust history in cardiac simulation. To this end, we have developed a computational simulation framework aimed at elucidating the effects of ischemia on measurable cardiac potentials. To validate our framework, we simulated, visualized, and analyzed 226 experimentally derived acute myocardial ischemic events. Simulation outcomes agreed both qualitatively (feature comparison) and quantitatively (correlation, average error, and significance) with experimentally obtained epicardial measurements, particularly under conditions of elevated ischemic stress. Our simulation framework introduces a novel approach to incorporating subject-specific, geometric models and experimental results that are highly resolved in space and time into computational models. We propose this framework as a means to advance the understanding of the underlying mechanisms of ischemic disease while simultaneously putting in place the computational infrastructure necessary to study and improve ischemia models aimed at reducing diagnostic errors in the clinic.



B.M. Burton, K.K. Aras, W.W. Good, J.D. Tate, B. Zenger, R.S. MacLeod. “Image-Based Modeling of Acute Myocardial Ischemia Using Experimentally Derived Ischemic Zone Source Representations,” In Journal of Electrocardiology, Vol. 51, No. 4, Elsevier BV, pp. 725--733. July, 2018.
DOI: 10.1016/j.jelectrocard.2018.05.005

ABSTRACT

Background

Computational models of myocardial ischemia often use oversimplified ischemic source representations to simulate epicardial potentials. The purpose of this study was to explore the influence of biophysically justified, subject-specific ischemic zone representations on epicardial potentials.

Methods

We developed and implemented an image-based simulation pipeline, using intramural recordings from a canine experimental model to define subject-specific ischemic regions within the heart. Static epicardial potential distributions, reflective of ST segment deviations, were simulated and validated against measured epicardial recordings.

Results

Simulated epicardial potential distributions showed strong statistical correlation and visual agreement with measured epicardial potentials. Additionally, we identified and described in what way border zone parameters influence epicardial potential distributions during the ST segment.

Conclusion

From image-based simulations of myocardial ischemia, we generated subject-specific ischemic sources that accurately replicated epicardial potential distributions. Such models are essential in understanding the underlying mechanisms of the bioelectric fields that arise during ischemia and are the basis for more sophisticated simulations of body surface ECGs.



M.J.M. Cluitmans, S. Ghimire, J. Dhamala, J. Coll-Font, J.D. Tate, S. Giffard-Roisin, J. Svehlikova, O. Doessel, M.S. Guillem, D.H. Brooks, R.S. Macleod, L. Wang. “P1125 Noninvasive localization of premature ventricular complexes: a research-community-based approach,” In EP Europace, Vol. 20, No. Supplement, Oxford University Press, March, 2018.
DOI: 10.1093/europace/euy015.611

ABSTRACT

Background: Noninvasive localization of premature ventricular complexes (PVCs) to guide ablation therapy is one of the emerging applications of electrocardiographic imaging (ECGI). Because of its increasing clinical use, it is essential to compare the many implementations of ECGI that exist to understand the specific characteristics of each approach.

Objective: Our consortium is a community of researchers aiming to collaborate in the field of ECGI, and to objectively compare and improve methods. Here, we will compare methods to localize the origin of PVCs with ECGI.

Methods: Our consortium hosts a repository of ECGI data on its website. For the current study, participants analysed simulated electrocardiograms from premature beats, freely available on that website. These PVCs were simulated to originate from eight ventricular locations and the resulting body-surface potentials were computed. These body-surface electrocardiograms (and the torso-heart geometry) were then provided to the study participants to apply their ECGI algorithms to determine the origin of the PVCs. Participants could choose freely among four different source models, i.e., representations of the bioelectric fields reconstructed from ECGI: 1) epicardial potentials (POTepi), 2) epicardial & endocardial potentials (POTepi&endo), 3) transmembrane potentials on the endocardium and epicardium (TMPepi&endo) and 4) transmembrame potentials throughout the myocardium (TMPmyo). Participants were free to employ any software implementation of ECGI and were blinded to the ground truth data.

Results: Four research groups submitted 11 entries for this study. The figure shows the localization error between the known and reconstructed origin of each PVC for each submission, categorized per source model. Each colour represents one research group and some groups submitted results using different approaches. These results demonstrate that the variation of accuracy was larger among research groups than among the source models. Most submissions achieved an error below 2 cm, but none performed with a consistent sub-centimetre accuracy.

Conclusion: This study demonstrates a successful community-based approach to study different ECGI methods for PVC localization. The goal was not to rank research groups but to compare both source models and numerical implementations. PVC localization with these methods was not as dependent on the source representation as it was on the implementation of ECGI. Consequently, ECGI validation should not be performed on generic methods, but should be specifically performed for each lab's implementation. The novelty of this study is that it achieves this in the first open, international comparison of approaches using a common set of gold standards. Continued collaborative validation is essential to understand the effect of implementation differences, in order to reach significant improvements and arrive at clinically-relevant sub-centimetre accuracy of PVC localization.



A. Prakosa, H. J. Arevalo, D. Deng, P. M. Boyle, P. P. Nikolov, H. Ashikaga, J. J. E. Blauer, E. Ghafoori, C. J. Park, R. C. Blake, F. T. Han, R. S. MacLeod, H. R. Halperin, D. J. Callans, R. Ranjan, J. Chrispin, S. Nazarian, N. A. Trayanova. “Personalized virtual-heart technology for guiding the ablation of infarct-related ventricular tachycardia,” In Nature Biomedical Engineering, Springer Nature America, Inc, September, 2018.
DOI: 10.1038/s41551-018-0282-2

ABSTRACT

Ventricular tachycardia (VT), which can lead to sudden cardiac death, occurs frequently in patients with myocardial infarction. Catheter-based radio-frequency ablation of cardiac tissue has achieved only modest efficacy, owing to the inaccurate identification of ablation targets by current electrical mapping techniques, which can lead to extensive lesions and to a prolonged, poorly tolerated procedure. Here, we show that personalized virtual-heart technology based on cardiac imaging and computational modelling can identify optimal infarct-related VT ablation targets in retrospective animal (five swine) and human studies (21 patients), as well as in a prospective feasibility study (five patients). We first assessed, using retrospective studies (one of which included a proportion of clinical images with artefacts), the capability of the technology to determine the minimum-size ablation targets for eradicating all VTs. In the prospective study, VT sites predicted by the technology were targeted directly, without relying on prior electrical mapping. The approach could improve infarct-related VT ablation guidance, where accurate identification of patient-specific optimal targets could be achieved on a personalized virtual heart before the clinical procedure.



A. Rodenhauser, W.W. Good, B. Zenger, J. Tate, K. Aras, B. Burton, R.S. Macleod. “PFEIFER: Preprocessing Framework for Electrograms Intermittently Fiducialized from Experimental Recordings,” In The Journal of Open Source Software, Vol. 3, No. 21, The Open Journal, pp. 472. Jan, 2018.
DOI: 10.21105/joss.00472

ABSTRACT

Preprocessing Framework for Electrograms Intermittently Fiducialized from Experimental Recordings (PFEIFER) is a MATLAB Graphical User Interface designed to process bioelectric signals acquired from experiments.

PFEIFER was specifically designed to process electrocardiographic recordings from electrodes placed on or around the heart or on the body surface. Specific steps included in PFEIFER allow the user to remove some forms of noise, correct for signal drift, and mark specific instants or intervals in time (fiducialize) within all of the time sampled channels. PFEIFER includes many unique features that allow the user to process electrical signals in a consistent and time efficient manner, with additional options for advanced user configurations and input. PFEIFER is structured as a consolidated framework that provides many standard processing pipelines but also has flexibility to allow the user to customize many of the steps. PFEIFER allows the user to import time aligned cardiac electrical signals, semi-automatically determine fiducial markings from those signals, and perform computational tasks that prepare the signals for subsequent display and analysis.