SCIENTIFIC COMPUTING AND IMAGING INSTITUTE
at the University of Utah

Central to the clinical adoption of patient-specific modeling strategies is demonstrating that simulation results are reliable and safe. Indeed, simulation frameworks must be robust to uncertainty in model input(s), and levels of confidence should accompany results. In this study, we applied a coupled uncertainty quantification-finite element (FE) framework to understand the impact of uncertainty in vascular material properties on variability in predicted stresses. Univariate probability distributions were fit to material parameters derived from layer-specific mechanical behavior testing of human coronary tissue. Parameters were assumed to be probabilistically independent, allowing for efficient parameter ensemble sampling. In an idealized coronary artery geometry, a forward FE model for each parameter ensemble was created to predict tissue stresses under physiologic loading. An emulator was constructed within the UncertainSCI software using polynomial chaos techniques, and statistics and sensitivities were directly computed. Results demonstrated that material parameter uncertainty propagates to variability in predicted stresses across the vessel wall, with the largest dispersions in stress within the adventitial layer. Variability in stress was most sensitive to uncertainties in the anisotropic component of the strain energy function. Moreover, unary and binary interactions within the adventitial layer were the main contributors to stress variance, and the leading factor in stress variability was uncertainty in the stress-like material parameter that describes the contribution of the embedded fibers to the overall artery stiffness. Results from a patient-specific coronary model confirmed many of these findings. Collectively, these data highlight the impact of material property variation on uncertainty in predicted artery stresses and present a pipeline to explore and characterize forward model uncertainty in computational biomechanics.

Background

Objective

Methods

Results

Conclusions

Because serum potassium and ECG morphology changes exhibit a well-understood connection, and the timeline of ECG changes can be relatively quick, there is motivation to explore the sensitivity of ML based prediction of serum potassium using 12 lead ECG data with respect to the time between the ECG and potassium readings.

We trained a convolutional neural network to classify abnormal (serum potassium above 5 mEq/L) vs normal (serum potassium between 4 and 5 mEq/L) from the ECG alone. We compared training with ECGs and potassium measurements filtered to be within 1 hour, 30 minutes, and 15 minutes of each other. We explored scenarios that both leveraged all available data at each time cutoff as well as restricted data to match training set sizes across the time cutoffs. For each case, we trained five separate instances of our neural network to account for variability.

The 1 hour cutoff with all data resulted in an average area under the receiver operator curve (AUC) of 0.850 and a weighted accuracy of 76.3%, 15 minutes resulted in 0.814, 72.5%, and 30 minutes. Truncating the training sets to the same size as the 15 minute cutoff results in comparable average accuracy and AUC for all. Our future studies will continue to explore the performance of ML potassium predictions through investigations of failure cases, identification of biases, and explainability analyses.

Background

The immediate impact of catheter ablation on left atrial mechanical function and the timeline for its recovery in patients undergoing ablation for atrial fibrillation (AF) remain uncertain. The mechanical function response to catheter ablation in patients with different AF types is poorly understood.

Methods

A total of 113 AF patients were included in this retrospective study. Each patient had three magnetic resonance imaging (MRI) studies in sinus rhythm: one pre-ablation, one immediate post-ablation (within 2 days after ablation), and one post-ablation follow-up MRI (≤ 3 months). We used feature tracking in the MRI cine images to determine peak longitudinal atrial strain (PLAS). We evaluated the change in strain from pre-ablation, immediately after ablation to post-ablation follow-up in a short-term study (< 50 days) and a 3-month study (3 months after ablation).

Results

The PLAS exhibited a notable reduction immediately after ablation, compared to both pre-ablation levels and those observed in follow-up studies conducted at short-term (11.1 ± 9.0 days) and 3-month (69.6 ± 39.6 days) intervals. However, there was no difference between follow-up and pre-ablation PLAS. The PLAS returned to 95% pre-ablation level within 10 days. Paroxysmal AF patients had significantly higher pre-ablation PLAS than persistent AF patients in pre-ablation MRIs. Both type AF patients had significantly lower immediate post-ablation PLAS compared with pre-ablation and post-ablation PLAS.

Conclusion

The present study suggested a significant drop in PLAS immediately after ablation. Left atrial mechanical function recovered within 10 days after ablation. The drop in PLAS did not show a substantial difference between paroxysmal and persistent AF patients.

Background

Radiofrequency balloon (RFB) ablation (HELIOSTAR™, Biosense Webster) has been developed to improve pulmonary vein ablation efficiency over traditional point-by-point RF ablation approaches. We aimed to find effective parameters for RFB ablation that result in chronic scar verified by late gadolinium enhancement cardiac magnetic resonance (LGE-CMR).

Methods

A chronic canine model (n = 8) was used to ablate in the superior vena cava (SVC), the right superior and the left inferior pulmonary vein (RSPV and LIPV), and the left atrial appendage (LAA) with a circumferential ablation approach (RF energy was delivered to all electrodes simultaneously) for 20 s or 60 s. The electroanatomical map with the ablation tags was projected onto the 3-month post-ablation LGE-CMR. Tags were divided into two groups depending on whether they correlated with CMR-based scar (ScarTags) or non-scar tissue (Non-ScarTags). The effective parameters for scar formation were estimated by multivariate logistic regression.

Results

This study assessed 80 lesions in the SVC, 80 lesions in the RSPV, 20 lesions in the LIPV, and 30 lesions in the LAA (168 ScarTags and 42 Non-ScarTags). In the multivariate analysis, two variables were associated with chronic scar formation: temperature of electrode before energy application (odds ratio (OR) 0.805, p = 0.0075) and long RF duration (OR 2.360, p = 0.0218), whereas impedance drop was not associated (OR 0.986, p = 0.373).

Conclusion

Lower temperature of the electrode before ablation and long ablation duration are critical parameters for durable atrial scar formation with RFB ablation.

Background

Late gadolinium-enhanced (LGE) MRI has become a widely used technique to non-invasively image the left atrium prior to catheter ablation. However, LGE-MRI images are prone to variable image quality, with quality metrics that do not necessarily correlate to the image’s diagnostic quality. In this study, we aimed to define consistent clinically relevant metrics for image and diagnostic quality in 3D LGE-MRI images of the left atrium, have multiple observers assess LGE-MRI image quality to identify key features that measure quality and intra/inter-observer variabilities, and train and test a CNN to assess image quality automatically.

Methods

We identified four image quality categories that impact fibrosis assessment in LGE-MRI images and trained individuals to score 50 consecutive pre-ablation atrial fibrillation LGE-MRI scans from the University of Utah hospital image database. The trained individuals then scored 146 additional scans, which were used to train a convolutional neural network (CNN) to assess diagnostic quality.

Results

There was excellent agreement among trained observers when scoring LGE-MRI scans, with inter-rater reliability scores ranging from 0.65 to 0.76 for each category. When the quality scores were converted to a binary diagnostic/non-diagnostic, the CNN achieved a sensitivity of http://www.w3.org/1998/Math/MathML"><mn>0.80</mn><mo>&#x00B1;</mo><mn>0.06</mn></math>" role="presentation">

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Conclusion

The use of a training document with reference examples helped raters achieve excellent agreement in their quality scores. The CNN gave a reasonably accurate classification of diagnostic or non-diagnostic 3D LGE-MRI images of the left atrium, despite the use of a relatively small training set.

Background

The role of fiber orientation on a global chamber level in sustaining atrial fibrillation (AF) is unknown. The goal of this study was to correlate the fiber direction derived from Diffusion Tensor Imaging (DTI) with AF inducibility.

Transgenic goats with cardiac-specific overexpression of constitutively active TGF-β1 (n = 14) underwent AF inducibility testing by rapid pacing in the left atrium. We chose a minimum of 10 minutes of sustained AF as a cut-off for AF inducibility. Explanted hearts underwent DTI to determine the fiber direction. Using tractography data, we clustered, visualized, and quantified the fiber helix angles in 8 different regions of the left atrial wall using two reference vectors defined based on anatomical landmarks.

Sustained AF was induced in 7 out of 14 goats. The mean helix fiber angles in 7 out of 8 selected regions were statistically different (P-Value < 0.05) in the AF inducible group. The average fractional anisotropy (FA) and the mean diffusivity (MD) were similar in the two groups with FA of 0.32±0.08 and MD of 8.54±1.72 mm2/s in the non-inducible group and FA of 0.31±0.05 (P-value = 0.90) and MD of 8.68±1.60 mm2/s (P-value = 0.88) in the inducible group.

DTI based fiber direction shows significant variability across subjects with a significant difference between animals that are AF inducible versus animals that are not inducible. Fiber direction might be contributing to the initiation and sustaining of AF, and its role needs to be investigated further.

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.

Electrocardiographic imaging (ECGI) is a clinical and research tool for noninvasive diagnosis of cardiac electrical dysfunction. The position of the heart within the torso is both an input and common source of error in ECGI. Many studies have sought to improve cardiac localization accuracy, however, few have examined quantitatively the effects of uncertainty in the position of the heart within the torso. Recently developed uncertainty quantification (UQ) tools enable the robust application of UQ to ECGI reconstructions. In this study, we developed an ECGI formulation, which for the first time, directly incorporated uncertainty in the heart position. The result is an ECGI solution that is robust to variation in heart position. Using data from two Langendorff experimental preparations, each with 120 heartbeats distributed across three activation sequences, we found that as heart position uncertainty increased above ±10 mm, the solution quality of the ECGI degraded. However, even at large heart position uncertainty (±40 mm) our novel UQ-ECGI formulation produced reasonable solutions (root mean squared error < 1 mV, spatial correlation >0.6, temporal correlation >0.75).

Previous studies have compared recorded torso potentials with electrocardiographic forward solutions from a pericardial cage. In this study, we introduce new comparisons of the forward solutions from the sock and cage with each other and with respect to the measured potentials on the torso. The forward problem of electrocardiographic imaging is expected to achieve high levels of accuracy since it is mathematically well posed. However, unexpectedly high residual errors remain between the computed and measured torso signals in experiments. A possible source of these errors is the limited spatial coverage of the cardiac sources in most experiments; most capture potentials only from the ventricles. To resolve the relationship between spatial coverage and the accuracy of the forward simulations, we combined two methods of capturing cardiac potentials using a 240-electrode sock and a 256-electrode cage, both surrounding a heart suspended in a 192-electrode torso tank. We analyzed beats from three pacing sites and calculated the RMSE, spatial correlation, and temporal correlation. We found that the forward solutions using the sock as the cardiac source were poorer compared to those obtained from the cage. In this study, we explore the differences in forward solution accuracy using the sock and the cage and suggest some possible explanations for these differences.

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).

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.
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.
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.

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.

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.
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.
Measuring the distance between the LA and the esophagus in pre-ablation LGE-MRI could be helpful in predicting ETI after LAPW ablation.

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.

Deep neural networks have shown promise in image reconstruction tasks, although often on the premise of large amounts of training data. In this paper, we present a new approach to exploit the geometry and physics underlying electrocardiographic imaging (ECGI) to learn efficiently with a relatively small dataset. We first introduce a non-Euclidean encoding-decoding network that allows us to describe the unknown and measurement variables over their respective geometrical domains. We then explicitly model the geometry-dependent physics in between the two domains via a bipartite graph over their graphical embeddings. We applied the resulting network to reconstruct electrical activity on the heart surface from body-surface potentials. In a series of generalization tasks with increasing difficulty, we demonstrated the improved ability of the network to generalize across geometrical changes underlying the data using less than 10% of training data and fewer variations of training geometry in comparison to its Euclidean alternatives. In both simulation and real-data experiments, we further demonstrated its ability to be quickly fine-tuned to new geometry using a modest amount of data.

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 (FA_max) 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 FA_max 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.

Background:

Computational biomedical simulations frequently contain parameters that model physical features, material coefficients, and physiological effects, whose values are typically assumed known a priori. Understanding the effect of variability in those assumed values is currently a topic of great interest. A general-purpose software tool that quantifies how variation in these parameters affects model outputs is not broadly available in biomedicine. For this reason, we developed the ‘UncertainSCI’ uncertainty quantification software suite to facilitate analysis of uncertainty due to parametric variability.

We developed and distributed a new open-source Python-based software tool, UncertainSCI, which employs advanced parameter sampling techniques to build polynomial chaos (PC) emulators that can be used to predict model outputs for general parameter values. Uncertainty of model outputs is studied by modeling parameters as random variables, and model output statistics and sensitivities are then easily computed from the emulator. Our approaches utilize modern, near-optimal techniques for sampling and PC construction based on weighted Fekete points constructed by subsampling from a suitably randomized candidate set.

Concentrating on two test cases—modeling bioelectric potentials in the heart and electric stimulation in the brain—we illustrate the use of UncertainSCI to estimate variability, statistics, and sensitivities associated with multiple parameters in these models.

UncertainSCI is a powerful yet lightweight tool enabling sophisticated probing of parametric variability and uncertainty in biomedical simulations. Its non-intrusive pipeline allows users to leverage existing software libraries and suites to accurately ascertain parametric uncertainty in a variety of applications.

Introduction: The inverse problem of ECGI is ill-posed, so regularization must be applied to constrain the solution. Regularization is typically applied to each individual time point (instantaneous) or to the beat as a whole (global). These techniques often lead to over- or underregularization. We aimed to develop an inverse formulation that strikes a balance between these two approaches that would realize the benefits of both by implementing a sliding-window regularization. Methods: We formulated sliding-window regularization using the boundary element method with Tikhonov 0 and 2nd order regularization. We applied regularization to a varying time window of the body-surface potentials centered around each time sample. We compared reconstructed potentials from the sliding-window, instantaneous, and global regularization techniques to ground truth potentials for 10 heart beats paced from the ventricle in a large-animal model. Results: The sliding-window technique provided smoother transitions of regularization weights than instantaneous regularization while improving spatial correlation over global regularization. Discussion: Although the differences in regularization weights were nuanced, smoother transitions provided by the sliding-window regularization have the ability to eliminate discontinuities in potential seen with instantaneous regularization.