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Nguyen TNT, Ballit A, Ferrandini M, Colliat JB, Dao TT. Fetus descent simulation with the active uterine contraction during the vaginal delivery: MRI-based evaluation and uncertainty quantification. Comput Methods Biomech Biomed Engin 2024:1-16. [PMID: 39256916 DOI: 10.1080/10255842.2024.2399777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Revised: 07/22/2024] [Accepted: 08/28/2024] [Indexed: 09/12/2024]
Abstract
Finite element models ranging from single to multiscale models have been widely used to gain valuable insights into the physiological delivery process and associated complication scenarios. However, the fetus descent simulation with the active uterine contraction is still challenging for validation and uncertainty quantification issues. The present study performed a fetus descent simulation using the active uterine contraction. Then, simulation outcomes were evaluated using theoretical and in vivo MRI childbirth data. Moreover, parameter uncertainty and propagation were also performed. A maternal pelvis model was developed. The active uterine contraction was modeled using a transversely isotropic Mooney-Rivlin material. Displacement trajectories were compared between simulation, theoretical and in vivo MRI childbirth data. Monte Carlo (M.C) and Polynomial Chaos Expansion (PCE) methods were applied to quantify uncertain parameters and their propagations. Obtained results showed that fetal descent behavior is consistent with the MRI-based observation as well as the theoretical trajectory (curve of Carus). The head downward vertical displacement ranges from 0 to approximately 47 mm. A reduction of 50% in uterine size was observed during the simulation. Three high-sensitive parameters (C 1 , C 2 , Ca 0 ) were also identified. Our study suggested that the use of the active uterine contraction is essential for simulating vaginal delivery but the global parameter sensitivity, parameter uncertainty, and outcome evaluation should be carefully performed. As a perspective, the developed approach could be extrapolated for patient-specific modeling and associated delivery complication simulations to identify risks and potential therapeutic solutions.
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Affiliation(s)
- Trieu-Nhat-Thanh Nguyen
- LaMcube - Laboratoire de Mécanique, Univ. Lille, CNRS, Centrale Lille, UMR 9013, Multiéchelle, Multiphysique, Lille, France
| | - Abbass Ballit
- LaMcube - Laboratoire de Mécanique, Univ. Lille, CNRS, Centrale Lille, UMR 9013, Multiéchelle, Multiphysique, Lille, France
| | - Morgane Ferrandini
- LaMcube - Laboratoire de Mécanique, Univ. Lille, CNRS, Centrale Lille, UMR 9013, Multiéchelle, Multiphysique, Lille, France
| | - Jean-Baptiste Colliat
- LaMcube - Laboratoire de Mécanique, Univ. Lille, CNRS, Centrale Lille, UMR 9013, Multiéchelle, Multiphysique, Lille, France
| | - Tien-Tuan Dao
- LaMcube - Laboratoire de Mécanique, Univ. Lille, CNRS, Centrale Lille, UMR 9013, Multiéchelle, Multiphysique, Lille, France
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2
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Nguyen TNT, Ballit A, Lecomte-Grosbras P, Colliat JB, Dao TT. On the uncertainty quantification of the active uterine contraction during the second stage of labor simulation. Med Biol Eng Comput 2024; 62:2145-2164. [PMID: 38478304 DOI: 10.1007/s11517-024-03059-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 02/23/2024] [Indexed: 06/21/2024]
Abstract
Uterine contractions in the myometrium occur at multiple scales, spanning both organ and cellular levels. This complex biological process plays an essential role in the fetus delivery during the second stage of labor. Several finite element models of active uterine contractions have already been developed to simulate the descent of the fetus through the birth canal. However, the developed models suffer severe reliability issues due to the uncertain parameters. In this context, the present study aimed to perform the uncertainty quantification (UQ) of the active uterine contraction simulation to advance our understanding of pregnancy mechanisms with more reliable indicators. A uterus model with and without fetus was developed integrating a transversely isotropic Mooney-Rivlin material with two distinct fiber orientation architectures. Different contraction patterns with complex boundary conditions were designed and applied. A global sensitivity study was performed to select the most valuable parameters for the uncertainty quantification (UQ) process using a copula-based Monte Carlo method. As results, four critical material parameters (C 1 , C 2 , K , Ca 0 ) of the active uterine contraction model were identified and used for the UQ process. The stress distribution on the uterus during the fetus descent, considering first and second fiber orientation families, ranged from 0.144 to 1.234 MPa and 0.044 to 1.619 MPa, respectively. The simulation outcomes revealed also the segment-specific contraction pattern of the uterus tissue. The present study quantified, for the first time, the effect of uncertain parameters of the complex constitutive model of the active uterine contraction on the fetus descent process. As perspectives, a full maternal pelvis model will be coupled with reinforcement learning to automatically identify the delivery mechanism behind the cardinal movements of the fetus during the active expulsion process.
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Affiliation(s)
- Trieu-Nhat-Thanh Nguyen
- Univ. Lille, CNRS, Centrale Lille, UMR 9013 LaMcube - Laboratoire de Mécanique, Multiphysique, Multiéchelle, 59000, Lille, France
| | - Abbass Ballit
- Univ. Lille, CNRS, Centrale Lille, UMR 9013 LaMcube - Laboratoire de Mécanique, Multiphysique, Multiéchelle, 59000, Lille, France
| | - Pauline Lecomte-Grosbras
- Univ. Lille, CNRS, Centrale Lille, UMR 9013 LaMcube - Laboratoire de Mécanique, Multiphysique, Multiéchelle, 59000, Lille, France
| | - Jean-Baptiste Colliat
- Univ. Lille, CNRS, Centrale Lille, UMR 9013 LaMcube - Laboratoire de Mécanique, Multiphysique, Multiéchelle, 59000, Lille, France
| | - Tien-Tuan Dao
- Univ. Lille, CNRS, Centrale Lille, UMR 9013 LaMcube - Laboratoire de Mécanique, Multiphysique, Multiéchelle, 59000, Lille, France.
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3
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Berggren CC, Jiang D, Jack Wang YF, Bergquist JA, Rupp LC, Liu Z, MacLeod RS, Narayan A, Timmins LH. Influence of material parameter variability on the predicted coronary artery biomechanical environment via uncertainty quantification. Biomech Model Mechanobiol 2024; 23:927-940. [PMID: 38361087 PMCID: PMC11102342 DOI: 10.1007/s10237-023-01814-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Accepted: 12/30/2023] [Indexed: 02/17/2024]
Abstract
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.
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Affiliation(s)
- Caleb C Berggren
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
| | - David Jiang
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
| | - Y F Jack Wang
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
| | - Jake A Bergquist
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
- Nora Eccles Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT, USA
| | - Lindsay C Rupp
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
- Nora Eccles Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT, USA
| | - Zexin Liu
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
- Department of Mathematics, University of Utah, Salt Lake City, UT, USA
| | - Rob S MacLeod
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
- Nora Eccles Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT, USA
| | - Akil Narayan
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
- Department of Mathematics, University of Utah, Salt Lake City, UT, USA
| | - Lucas H Timmins
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA.
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA.
- School of Engineering Medicine, Texas A&M University, 1020 Holcombe Blvd., Houston, TX, USA.
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA.
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Berggren CC, Jiang D, Jack Wang Y, Bergquist JA, Rupp LC, Liu Z, MacLeod RS, Narayan A, Timmins LH. Influence of Material Parameter Variability on the Predicted Coronary Artery Biomechanical Environment via Uncertainty Quantification. ARXIV 2024:arXiv:2401.15047v1. [PMID: 38344225 PMCID: PMC10854278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Abstract
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.
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Affiliation(s)
- Caleb C. Berggren
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
| | - David Jiang
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
| | - Y.F. Jack Wang
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
| | - Jake A. Bergquist
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
- Nora Eccles Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT, USA
| | - Lindsay C. Rupp
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
- Nora Eccles Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT, USA
| | - Zexin Liu
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
- Department of Mathematics, University of Utah, Salt Lake City, UT, USA
| | - Rob S. MacLeod
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
- Nora Eccles Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT, USA
| | - Akil Narayan
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
- Department of Mathematics, University of Utah, Salt Lake City, UT, USA
| | - Lucas H. Timmins
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
- School of Engineering Medicine, Texas A&M University, Houston, TX, USA
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA
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5
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Bergquist JA, Zenger B, Rupp LC, Busatto A, Tate J, Brooks DH, Narayan A, MacLeod RS. Uncertainty quantification of the effect of cardiac position variability in the inverse problem of electrocardiographic imaging. Physiol Meas 2023; 44:105003. [PMID: 37734339 DOI: 10.1088/1361-6579/acfc32] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 09/21/2023] [Indexed: 09/23/2023]
Abstract
Objective.Electrocardiographic imaging (ECGI) is a functional imaging modality that consists of two related problems, the forward problem of reconstructing body surface electrical signals given cardiac bioelectric activity, and the inverse problem of reconstructing cardiac bioelectric activity given measured body surface signals. ECGI relies on a model for how the heart generates bioelectric signals which is subject to variability in inputs. The study of how uncertainty in model inputs affects the model output is known as uncertainty quantification (UQ). This study establishes develops, and characterizes the application of UQ to ECGI.Approach.We establish two formulations for applying UQ to ECGI: a polynomial chaos expansion (PCE) based parametric UQ formulation (PCE-UQ formulation), and a novel UQ-aware inverse formulation which leverages our previously established 'joint-inverse' formulation (UQ joint-inverse formulation). We apply these to evaluate the effect of uncertainty in the heart position on the ECGI solutions across a range of ECGI datasets.Main results.We demonstrated the ability of our UQ-ECGI formulations to characterize the effect of parameter uncertainty on the ECGI inverse problem. We found that while the PCE-UQ inverse solution provided more complex outputs such as sensitivities and standard deviation, the UQ joint-inverse solution provided a more interpretable output in the form of a single ECGI solution. We find that between these two methods we are able to assess a wide range of effects that heart position variability has on the ECGI solution.Significance.This study, for the first time, characterizes in detail the application of UQ to the ECGI inverse problem. We demonstrated how UQ can provide insight into the behavior of ECGI using variability in cardiac position as a test case. This study lays the groundwork for future development of UQ-ECGI studies, as well as future development of ECGI formulations which are robust to input parameter variability.
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Affiliation(s)
- Jake A Bergquist
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, United States of America
- Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah, SLC, UT, United States of America
- Department of Biomedical Engineering, University of Utah, SLC, UT, United States of America
| | - Brian Zenger
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, United States of America
- Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah, SLC, UT, United States of America
- Department of Biomedical Engineering, University of Utah, SLC, UT, United States of America
- School of Medicine, University of Utah, SLC, UT, United States of America
| | - Lindsay C Rupp
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, United States of America
- Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah, SLC, UT, United States of America
- Department of Biomedical Engineering, University of Utah, SLC, UT, United States of America
| | - Anna Busatto
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, United States of America
- Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah, SLC, UT, United States of America
- Department of Biomedical Engineering, University of Utah, SLC, UT, United States of America
| | - Jess Tate
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, United States of America
| | - Dana H Brooks
- Department of Electrical and Computer Engineering, Northeastern University, United States of America
| | - Akil Narayan
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, United States of America
- Department of Mathematics, University of Utah, SLC, UT, United States of America
| | - Rob S MacLeod
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, United States of America
- Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah, SLC, UT, United States of America
- Department of Biomedical Engineering, University of Utah, SLC, UT, United States of America
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Busatto A, Rupp LC, Gillette K, Narayan A, Plank G, MacLeod RS. Capturing the Influence of Conduction Velocity on Epicardial Activation Patterns Using Uncertainty Quantification. COMPUTING IN CARDIOLOGY 2023; 50:10.22489/cinc.2023.345. [PMID: 39193482 PMCID: PMC11349309 DOI: 10.22489/cinc.2023.345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/29/2024]
Abstract
Individual variability in parameter settings, due to either user selection or disease states, can impact accuracy when simulating the electrical behavior of the heart. Here, we aim to test the impact of inevitable uncertainty in conduction velocities (CVs) on the output of simulations of cardiac propagation, given three stimulus locations on the left ventricular (LV) free wall. To understand the role of physiological variability in CV in simulations of cardiac activation, we generated detailed maps of the variability in propagation simulations by implementing bi-ventricular activation simulations and quantified the effects by deploying robust uncertainty quantification techniques based on polynomial chaos expansion (PCE). PCE allows efficient stochastic exploration with reduced computational demand by utilizing an emulator for the underlying forward model. Our results suggest that CV within healthy physiological ranges plays a small role in the activation times across all stimulation locations. However, we noticed differences in variation coefficients depending on the stimulation site, i.e., LV endocardium, midmyocardium, and epicardium. We observed low levels of variation in activation times near the earliest activation sites, whereas there was higher variation toward the termination sites. These results suggest that CV variability can play a role when simulating healthy and diseased states.
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Affiliation(s)
- Anna Busatto
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA
- Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA
- Department of Biomedical Engineering, University of Utah, SLC, UT, USA
| | - Lindsay C Rupp
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA
- Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA
- Department of Biomedical Engineering, University of Utah, SLC, UT, USA
| | - Karli Gillette
- Institute of Biophysics, Medical University of Graz, Graz, Austria
| | - Akil Narayan
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA
- Department of Mathematics, University of Utah, SLC, UT, USA
| | - Gernot Plank
- Institute of Biophysics, Medical University of Graz, Graz, Austria
| | - Rob S MacLeod
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA
- Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA
- Department of Biomedical Engineering, University of Utah, SLC, UT, USA
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7
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Bergquist JA, Lange M, Zenger B, Orkild B, Paccione E, Kwan E, Hunt B, Dong J, MacLeod RS, Narayan A, Ranjan R. Uncertainty Quantification of the Effect of Variable Conductivity in Ventricular Fibrotic Regions on Ventricular Tachycardia. COMPUTING IN CARDIOLOGY 2023; 50:10.22489/cinc.2023.141. [PMID: 39193484 PMCID: PMC11349308 DOI: 10.22489/cinc.2023.141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/29/2024]
Abstract
Ventricular tachycardia (VT) is a life-threatening cardiac arrhythmia for which a common treatment pathway is electroanatomical mapping and ablation. Recent advances in both noninvasive ablation techniques and computational modeling have motivated the development of patient-specific computational models of VT. Such models are parameterized by a wide range of inputs, each of which is associated with an often unknown amount of error and uncertainty. Uncertainty quantification (UQ) is a technique to assess how variability in the inputs to a model affects its outputs. UQ has seen increased attention in computational cardiology as an avenue to further improve, understand, and develop patient-specific models. In this study we applied polynomial chaos-based UQ to explore the effect of varying the tissue conductivity of fibrotic border zones in a patient-specific model on the resulting VT simulation. We found that over a range of inputs, the model was most sensitive to fibrotic sheet direction, and uncertainty in fibrotic conductivity resulted in substantial variability in the VT reentry duration and cycle length. Overall, this study paves the way for future UQ applications to improve and understand VT models.
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Affiliation(s)
- Jake A Bergquist
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA
- Nora Eccles Treadwell CVRTI, University of Utah, SLC, UT, USA
- Department of Biomedical Engineering, University of Utah, SLC, UT, USA
| | - Matthias Lange
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA
- Nora Eccles Treadwell CVRTI, University of Utah, SLC, UT, USA
- Department of Biomedical Engineering, University of Utah, SLC, UT, USA
| | - Brian Zenger
- Department of Internal Medicine, Washington University in St Louis, St Louis, MO, USA
| | - Ben Orkild
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA
- Nora Eccles Treadwell CVRTI, University of Utah, SLC, UT, USA
- Department of Biomedical Engineering, University of Utah, SLC, UT, USA
| | - Eric Paccione
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA
- Nora Eccles Treadwell CVRTI, University of Utah, SLC, UT, USA
- Department of Biomedical Engineering, University of Utah, SLC, UT, USA
| | - Eugene Kwan
- Nora Eccles Treadwell CVRTI, University of Utah, SLC, UT, USA
- Department of Biomedical Engineering, University of Utah, SLC, UT, USA
| | - Bram Hunt
- Nora Eccles Treadwell CVRTI, University of Utah, SLC, UT, USA
- Department of Biomedical Engineering, University of Utah, SLC, UT, USA
| | - Jiawei Dong
- Nora Eccles Treadwell CVRTI, University of Utah, SLC, UT, USA
- Department of Biomedical Engineering, University of Utah, SLC, UT, USA
| | - Rob S MacLeod
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA
- Nora Eccles Treadwell CVRTI, University of Utah, SLC, UT, USA
- Department of Biomedical Engineering, University of Utah, SLC, UT, USA
| | - Akil Narayan
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA
| | - Ravi Ranjan
- Nora Eccles Treadwell CVRTI, University of Utah, SLC, UT, USA
- Department of Biomedical Engineering, University of Utah, SLC, UT, USA
- Department of Internal Medicine, Washington University in St Louis, St Louis, MO, USA
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8
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Rupp LC, Busatto A, Bergquist JA, Gillette K, Narayan A, Plank G, MacLeod RS. Uncertainty Quantification of Fiber Orientation and Epicardial Activation. COMPUTING IN CARDIOLOGY 2023; 50:10.22489/cinc.2023.137. [PMID: 39193483 PMCID: PMC11349307 DOI: 10.22489/cinc.2023.137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/29/2024]
Abstract
Predictive models and simulations of cardiac function require accurate representations of anatomy, often to the scale of local myocardial fiber structure. However, acquiring this information in a patient-specific manner is challenging. Moreover, the impact of physiological variability in fiber orientation on simulations of cardiac activation is poorly understood. To explore these effects, we implemented bi-ventricular activation simulations using rule-based fiber algorithms and robust uncertainty quantification techniques to generate detailed maps of model variability. Specifically, we utilized polynomial chaos expansion, enabling efficient exploration with reduced computational demand through an emulator function approximating the underlying forward model. Our study focused on examining the epicardial activation sequences of the heart in response to six stimuli locations and five metrics of activation. Our findings revealed that physiological variability in fiber orientation does not significantly affect the location of activation features, but it does impact the overall spread of activation. We observed low variability near the earliest activation sites, but high variability across the rest of the epicardial surface. We conclude that the level of accuracy of myocardial fiber orientation required for simulation depends on the specific goals of the model and the related research or clinical goals.
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Affiliation(s)
- Lindsay C Rupp
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA
- Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA
- Department of Biomedical Engineering, University of Utah, SLC, UT, USA
| | - Anna Busatto
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA
- Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA
- Department of Biomedical Engineering, University of Utah, SLC, UT, USA
| | - Jake A Bergquist
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA
- Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA
- Department of Biomedical Engineering, University of Utah, SLC, UT, USA
| | - Karli Gillette
- Institute of Biophysics, Medical University of Graz, Graz, Austria
| | - Akil Narayan
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA
- Department of Mathematics, University of Utah, SLC, UT, USA
| | - Gernot Plank
- Institute of Biophysics, Medical University of Graz, Graz, Austria
| | - Rob S MacLeod
- Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA
- Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA
- Department of Biomedical Engineering, University of Utah, SLC, UT, USA
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9
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Jimenez-Coll V, Llorente S, Boix F, Alfaro R, Galián JA, Martinez-Banaclocha H, Botella C, Moya-Quiles MR, Muro-Pérez M, Minguela A, Legaz I, Muro M. Monitoring of Serological, Cellular and Genomic Biomarkers in Transplantation, Computational Prediction Models and Role of Cell-Free DNA in Transplant Outcome. Int J Mol Sci 2023; 24:ijms24043908. [PMID: 36835314 PMCID: PMC9963702 DOI: 10.3390/ijms24043908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 02/03/2023] [Accepted: 02/05/2023] [Indexed: 02/17/2023] Open
Abstract
The process and evolution of an organ transplant procedure has evolved in terms of the prevention of immunological rejection with the improvement in the determination of immune response genes. These techniques include considering more important genes, more polymorphism detection, more refinement of the response motifs, as well as the analysis of epitopes and eplets, its capacity to fix complement, the PIRCHE algorithm and post-transplant monitoring with promising new biomarkers that surpass the classic serum markers such as creatine and other similar parameters of renal function. Among these new biomarkers, we analyze new serological, urine, cellular, genomic and transcriptomic biomarkers and computational prediction, with particular attention to the analysis of donor free circulating DNA as an optimal marker of kidney damage.
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Affiliation(s)
- Víctor Jimenez-Coll
- Immunology Service, Instituto Murciano de Investigación Biosanitaria (IMIB), Hospital Clínico Universitario Virgen de la Arrixaca (HCUVA), 30120 Murcia, Spain
| | - Santiago Llorente
- Nephrology Service, Instituto Murciano de Investigación Biosanitaria (IMIB), Hospital Clínico Universitario Virgen de la Arrixaca (HCUVA), 30120 Murcia, Spain
| | - Francisco Boix
- Immunology Service, Instituto Murciano de Investigación Biosanitaria (IMIB), Hospital Clínico Universitario Virgen de la Arrixaca (HCUVA), 30120 Murcia, Spain
| | - Rafael Alfaro
- Immunology Service, Instituto Murciano de Investigación Biosanitaria (IMIB), Hospital Clínico Universitario Virgen de la Arrixaca (HCUVA), 30120 Murcia, Spain
| | - José Antonio Galián
- Immunology Service, Instituto Murciano de Investigación Biosanitaria (IMIB), Hospital Clínico Universitario Virgen de la Arrixaca (HCUVA), 30120 Murcia, Spain
| | - Helios Martinez-Banaclocha
- Immunology Service, Instituto Murciano de Investigación Biosanitaria (IMIB), Hospital Clínico Universitario Virgen de la Arrixaca (HCUVA), 30120 Murcia, Spain
| | - Carmen Botella
- Immunology Service, Instituto Murciano de Investigación Biosanitaria (IMIB), Hospital Clínico Universitario Virgen de la Arrixaca (HCUVA), 30120 Murcia, Spain
| | - María R. Moya-Quiles
- Immunology Service, Instituto Murciano de Investigación Biosanitaria (IMIB), Hospital Clínico Universitario Virgen de la Arrixaca (HCUVA), 30120 Murcia, Spain
| | - Manuel Muro-Pérez
- Immunology Service, Instituto Murciano de Investigación Biosanitaria (IMIB), Hospital Clínico Universitario Virgen de la Arrixaca (HCUVA), 30120 Murcia, Spain
| | - Alfredo Minguela
- Immunology Service, Instituto Murciano de Investigación Biosanitaria (IMIB), Hospital Clínico Universitario Virgen de la Arrixaca (HCUVA), 30120 Murcia, Spain
| | - Isabel Legaz
- Department of Legal and Forensic Medicine, Biomedical Research Institute (IMIB), Regional Campus of International Excellence “Campus Mare Nostrum”, Faculty of Medicine, University of Murcia, 30100 Murcia, Spain
- Correspondence: (I.L.); (M.M.); Tel.: +34-699986674 (M.M.); Fax: +34-868834307 (M.M.)
| | - Manuel Muro
- Immunology Service, Instituto Murciano de Investigación Biosanitaria (IMIB), Hospital Clínico Universitario Virgen de la Arrixaca (HCUVA), 30120 Murcia, Spain
- Correspondence: (I.L.); (M.M.); Tel.: +34-699986674 (M.M.); Fax: +34-868834307 (M.M.)
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