Noninvasive Reconstruction of Transmural Transmembrane Potential With Simultaneous Estimation of Prior Model Error

Ghimire, Sandesh; Sapp, John L.; Horáček, B. Milan; Wang, Linwei

doi:10.1109/tmi.2019.2906600

Cited by 11 publications

(10 citation statements)

References 28 publications

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“…Probabilistic methods rely on Bayesian inference theory and numerical techniques to generate posterior distributions for the model parameters [37], [38]. They can incorporate prior knowledge into the parameter estimation with uncertainty, which can be used to guide decision-making and assess the robustness of the results [25]. Nevertheless, conventional probabilistic methods are usually computationally expensive, as repeated numerical simulations are required to generate samples for the posterior distribution.…”

Section: Solving the Electrocardiography Inverse Problemmentioning

confidence: 99%

Toward Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference

Li,

Camps,

Jenny Wang

et al. 2024

IEEE Trans. Med. Imaging

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Cardiac digital twins (CDTs) have the potential to offer individualized evaluation of cardiac function in a non-invasive manner, making them a promising approach for personalized diagnosis and treatment planning of myocardial infarction (MI). The inference of accurate myocardial tissue properties is crucial in creating a reliable CDT of MI. In this work, we investigate the feasibility of inferring myocardial tissue properties from the electrocardiogram (ECG) within a CDT platform. The platform integrates multimodal data, such as cardiac MRI and ECG, to enhance the accuracy and reliability of the inferred tissue properties. We perform a sensitivity analysis based on computer simulations, systematically exploring the effects of infarct location, size, degree of transmurality, and electrical activity alteration on the simulated QRS complex of ECG, to establish the limits of the approach. We subsequently present a novel deep computational model, comprising a dualbranch variational autoencoder and an inference model, to infer infarct location and distribution from the simulated QRS. The proposed model achieves mean Dice scores of 0.457 ± 0.317 and 0.302 ± 0.273 for the inference of left ventricle scars and border zone, respectively. The sensitivity analysis enhances our understanding of the complex relationship between infarct characteristics and electrophysiological features. The in silico experimental results show that the model can effectively capture the relationship for the inverse inference, with promising potential for clinical application in the future. The code is available at https: //github.com/lileitech/MI_inverse_inference.

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Section: Solving the Electrocardiography Inverse Problemmentioning

confidence: 99%

Toward Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference

Li,

Camps,

Jenny Wang

et al. 2024

IEEE Trans. Med. Imaging

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show abstract

“…In this framework, we can easily integrate two measurements from different domains by defining a prior distribution which behaves like a Gaussian in a gradient domain, i.e., whose derivative is Gaussian. Using this approach, we can derive a Kalman update equation (as in [Ghimire et al 2019]).…”

Section: Problem Formulationmentioning

confidence: 99%

“…Note that we have used the spatial gradient of the iris position. For a prior distribution, the low dimensional structure of the signal can be considered to model the prediction error [Ghimire et al 2019]. In our case, we find the hybrid signal by minimizing the mean square estimation between hybrid velocity and iris velocity and also between hybrid position and pupil position.…”

mentioning

confidence: 99%

Enhancing the precision of remote eye-tracking using iris velocity estimation

Chaudhary

Pelz

2021

ACM Symposium on Eye Tracking Research and Applications

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A new high-precision eye-tracking method has been demonstrated recently by tracking the motion of iris features rather than by exploiting pupil edges. While the method provides high precision, it suffers from temporal drift, an inability to track across blinks, and loss of texture matches in the presence of motion blur. In this work, we present a new methodology (π t ) to address these issues by optimally combining the information from both iris textures and pupil edges. With this method, we show an improvement in precision (S2S-RMS & STD) of at least 48% and 10% respectively while fixating a series of small targets and following a smoothly moving target. Further, we demonstrate the capability in the identification of microsaccades between targets separated by 0.2 • .

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“…Probabilistic methods rely on Bayesian inference theory and numerical techniques to generate posterior distributions for the model parameters [17], [18]. They can incorporate prior knowledge into the parameter estimation with an uncertainty, which can be used to guide decision-making and assess the robustness of the results [19]. Nevertheless, conventional probabilistic methods are usually computationally expensive, as repeated numerical simulations are required to generate samples for the posterior distribution.…”

Section: Introductionmentioning

confidence: 99%

Deep Computational Model for the Inference of Ventricular Activation Properties

Camps

Banerjee

et al. 2022

Lecture Notes in Computer Science

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Myocardial infarction (MI) demands precise and swift diagnosis. Cardiac digital twins (CDTs) have the potential to offer individualized evaluation of cardiac function in a non-invasive manner, making them a promising approach for personalized diagnosis and treatment planning of MI. The inference of accurate myocardial tissue properties is crucial in creating a reliable CDT platform, and particularly in the context of studying MI. In this work, we investigate the feasibility of inferring myocardial tissue properties from the electrocardiogram (ECG), focusing on the development of a comprehensive CDT platform specifically designed for MI.The platform integrates multi-modal data, such as cardiac MRI and ECG, to enhance the accuracy and reliability of the inferred tissue properties. We perform a sensitivity analysis based on computer simulations, systematically exploring the effects of infarct location, size, degree of transmurality, and electrical activity alteration on the simulated QRS complex of ECG, to establish the limits of the approach. We subsequently propose a deep computational model to infer infarct location and distribution from the simulated QRS. The in silico experimental results show that our model can effectively capture the complex relationships between the QRS signals and the corresponding infarct regions, with promising potential for clinical application in the future. The code will be released publicly once the manuscript is accepted for publication.

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Noninvasive Reconstruction of Transmural Transmembrane Potential With Simultaneous Estimation of Prior Model Error

Cited by 11 publications

References 28 publications

Toward Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference

Toward Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference

Enhancing the precision of remote eye-tracking using iris velocity estimation

Deep Computational Model for the Inference of Ventricular Activation Properties

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