Inverse modeling framework for characterizing patient-specific microstructural changes in the pulmonary arteries

Pourmodheji, Reza; Jiang, Zhenxiang; Tossas-Betancourt, Christopher; Figueroa, C. Alberto; Baek, Seungik; Lee, Lik-Chuan

doi:10.1016/j.jmbbm.2021.104448

Cited by 4 publications

(8 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While methods to reconstruct the geometry and estimate local deformations of cardiovascular tissues from in vivo images are emerging, 7,40,41 the spatial and temporal resolution provided by current technology is not yet sufficient to eliminate the need for ex vivo pDIC‐ and OCT‐based measurements in murine samples. Moreover, estimation of local material parameters directly from in vivo images remains difficult, even in human subjects which have considerably larger vessel geometries 42–46 …”

Section: Discussionmentioning

confidence: 99%

Uncertainty quantification in subject‐specific estimation of local vessel mechanical properties

Rego

Weiss

Bersi

et al. 2021

Numer Methods Biomed Eng

View full text Add to dashboard Cite

Quantitative estimation of local mechanical properties remains critically important in the ongoing effort to elucidate how blood vessels establish, maintain, or lose mechanical homeostasis. Recent advances based on panoramic digital image correlation (pDIC) have made high‐fidelity 3D reconstructions of small‐animal (e.g., murine) vessels possible when imaged in a variety of quasi‐statically loaded configurations. While we have previously developed and validated inverse modeling approaches to translate pDIC‐measured surface deformations into biomechanical metrics of interest, our workflow did not heretofore include a methodology to quantify uncertainties associated with local point estimates of mechanical properties. This limitation has compromised our ability to infer biomechanical properties on a subject‐specific basis, such as whether stiffness differs significantly between multiple material locations on the same vessel or whether stiffness differs significantly between multiple vessels at a corresponding material location. In the present study, we have integrated a novel uncertainty quantification and propagation pipeline within our inverse modeling approach, relying on empirical and analytic Bayesian techniques. To demonstrate the approach, we present illustrative results for the ascending thoracic aorta from three mouse models, quantifying uncertainties in constitutive model parameters as well as circumferential and axial tangent stiffness. Our extended workflow not only allows parameter uncertainties to be systematically reported, but also facilitates both subject‐specific and group‐level statistical analyses of the mechanics of the vessel wall.

show abstract

Section: Discussionmentioning

confidence: 99%

Uncertainty quantification in subject‐specific estimation of local vessel mechanical properties

Rego

Weiss

Bersi

et al. 2021

Numer Methods Biomed Eng

View full text Add to dashboard Cite

show abstract

“…Moreover, estimation of local material parameters directly from in vivo images remains difficult, even in human subjects which have a considerably larger vessel geometries. [41][42][43][44][45] An additional caveat in the application of the presented methods is that the form of the constitutive model is assumed to be true for the purposes of parameter estimation and uncertainty quantification. This assumption carries an important limitation:…”

Section: Discussionmentioning

confidence: 99%

Uncertainty quantification in subject-specific estimation of local vessel mechanical properties

Rego

Weiss

Bersi

et al. 2021

Preprint

View full text Add to dashboard Cite

Quantitative estimation of local mechanical properties remains critically important in the ongoing effort to elucidate how blood vessels establish, maintain, or lose mechanical homeostasis. Recent advances based on panoramic digital image correlation (pDIC) have made high-fidelity 3D reconstructions of small-animal (e.g., murine) vessels possible when imaged in a variety of quasi-statically loaded configurations. While we have previously developed and validated inverse modeling approaches to translate pDIC-measured surface deformations into biomechanical metrics of interest, our workflow did not heretofore include a methodology to quantify uncertainties associated with local point estimates of mechanical properties. This limitation has compromised our ability to infer biomechanical properties on a subject-specific basis, such as whether stiffness differs significantly between multiple material locations on the same vessel or whether stiffness differs significantly between multiple vessels at a corresponding material location. In the present study, we have integrated a novel uncertainty quantification and propagation pipeline within our inverse modeling approach, relying on empirical and analytic Bayesian techniques. To demonstrate the approach, we present illustrative results for the ascending thoracic aorta from three mouse models, quantifying uncertainties in constitutive model parameters as well as circumferential and axial tangent stiffness. Our extended workflow not only allows parameter uncertainties to be systematically reported, but also facilitates both subject-specific and group-level statistical analyses of the mechanics of the vessel wall.

show abstract

“…Vascular mechanical properties of the PA, including the intrinsic stiffness and the deposition stretches, were prescribed based from the authors’ previous work on the same PAH patient [13]. Due to a lack of data, removal rate of the PA constituents (kqα) and the PA mass density per unit volume (ρ) were set to those associated with the abdominal aortic tissue constituents in Zeinali-Davarani et al .…”

Section: Methodsmentioning

confidence: 99%

“… a Patient-specific acquired by the MR images. b General estimation by the previous studies [9, 20, 27]. c Patient-specific by optimization in the previous study [13]. d Calibration to fit the patient-specific clinical P–V relationship of PA and RV.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Computational modelling of multi-temporal ventricular–vascular interactions during the progression of pulmonary arterial hypertension

Pourmodheji

Jiang

Tossas-Betancourt

et al. 2022

J. R. Soc. Interface.

Self Cite

View full text Add to dashboard Cite

A computational framework is developed to consider the concurrent growth and remodelling (G&R) processes occurring in the large pulmonary artery (PA) and right ventricle (RV), as well as ventricular–vascular interactions during the progression of pulmonary arterial hypertension (PAH). This computational framework couples the RV and the proximal PA in a closed-loop circulatory system that operates in a short timescale of a cardiac cycle, and evolves over a long timescale due to G&R processes in the PA and RV. The framework predicts changes in haemodynamics (e.g. 68.2% increase in mean PA pressure), RV geometry (e.g. 38% increase in RV end-diastolic volume) and PA tissue microstructure (e.g. 90% increase in collagen mass) that are consistent with clinical and experimental measurements of PAH. The framework also predicts that a reduction in RV contractility is associated with long-term RV chamber dilation, a common biomarker observed in the late-stage PAH. Sensitivity analyses on the G&R rate constants show that large PA stiffening (both short and long term) is affected by RV remodelling more than the reverse. This framework can serve as a foundation for the future development of a more predictive and comprehensive cardiovascular G&R model with realistic heart and vascular geometries.

show abstract

Inverse modeling framework for characterizing patient-specific microstructural changes in the pulmonary arteries

Cited by 4 publications

References 64 publications

Uncertainty quantification in subject‐specific estimation of local vessel mechanical properties

Uncertainty quantification in subject‐specific estimation of local vessel mechanical properties

Uncertainty quantification in subject-specific estimation of local vessel mechanical properties

Computational modelling of multi-temporal ventricular–vascular interactions during the progression of pulmonary arterial hypertension

Contact Info

Product

Resources

About