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    Computational modeling of vascular growth in patient-specific pulmonary arterial patch reconstructions
    (Elsevier Science, 2021) Lashkarinia, S. Samaneh; Çoban, Gürsan; Köse, Banu; Salihoğlu, Ece; Pekkan, Kerem
    Recent progress in vascular growth mechanics has involved the use of computational algorithms toaddress clinical problems with the use of three-dimensional patient specific geometries. The objectiveof this study is to establish a predictive computational model for the volumetric growth of pulmonaryarterial (PA) tissue following complex cardiovascular patch reconstructive surgeries for congenital heartdisease patients. For the first time in the literature, the growth mechanics and performance of artificialcardiovascular patches in contact with the growing PA tissue domain is established. An elastic-growing material model was developed in the open source FEBio software suite to first examine the sur-gical patch reconstruction process for an idealized main PA anatomy as a benchmark model and then forthe patient-specific PA of a newborn. Following patch reconstruction, high levels of stress and strain arecompensated by growth on the arterial tissue. As this growth progresses, the arterial tissue is predicted tostiffen to limit elastic deformations. We simulated this arterial growth up to the age of 18 years, whensomatic growth plateaus. Our research findings show that the non-growing patch material remains ina low strain state throughout the simulation timeline, while experiencing high stress hot-spots.Arterial tissue growth along the surgical stitch lines is triggered mainly due to PA geometry and bloodpressure, rather than due to material property differences in the artificial and native tissue. Thus, non-uniform growth patterns are observed along the arterial tissue proximal to the sutured boundaries.This computational approach is effective for the pre-surgical planning of complex patch surgeries toquantify the unbalanced growth of native arteries and artificial non-growing materials to develop opti-mal patch biomechanics for improved postoperative outcomes.
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    Embryonic aortic arch material properties obtained by optical coherence tomography-guided micropipette aspiration
    (Elsevier, 2022) Lashkarinia, S.Samaneh; Çoban, Gürsan; Banu Siddiqui, Hummaira; Hwai Yap, Choon; Pekkan, Kerem
    It is challenging to determine the in vivo material properties of a very soft, mesoscale arterial vesselsof size ? 80 to 120 ?m diameter. This information is essential to understand the early embryonic cardiovascular development featuring rapidly evolving dynamic microstructure. Previous research efforts to describe the properties of the embryonic great vessels are very limited. Our objective is to measure the local material properties of pharyngeal aortic arch tissue of the chick-embryo during the early Hamburger-Hamilton (HH) stages, HH18 and HH24. Integrating the micropipette aspiration technique with optical coherence tomography (OCT) imaging, a clear vision of the aspirated arch geometry is achieved for an inner pipette radius of Rp = 25 ?m. The aspiration of this region is performed through a calibrated negatively pressurized micro-pipette. A computational finite element model is developed to model the nonlinear behaviour of the arch structure by considering the geometry-dependent constraints. Numerical estimations of the nonlinear material parameters for aortic arch samples are presented. The exponential material nonlinearity parameter (a) of aortic arch tissue increases statistically significantly from a = 0.068 ± 0.013 at HH18 to a = 0.260 ± 0.014 at HH24 (p = 0.0286). As such, the aspirated tissue length decreases from 53 ?m at HH18 to 34 ?m at HH24. The calculated NeoHookean shear modulus increases from 51 Pa at HH18 to 93 Pa at HH24 which indicates a statistically significant stiffness increase. These changes are due to the dynamic changes of collagen and elastin content in the media layer of the vessel during development. © 2022 Elsevier Ltd
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    Mechanical characterization and torsional buckling of pediatric cardiovascular materials
    (Springer Heidelberg, 2024) Donmazov, Samir; Piskin, Senol; Golcez, Tansu; Kul, Demet; Arnaz, Ahmet; Pekkan, Kerem
    In complex cardiovascular surgical reconstructions, conduit materials that avoid possible large-scale structural deformations should be considered. A fundamental mode of mechanical complication is torsional buckling which occurs at the anastomosis site due to the mechanical instability, leading surgical conduit/patch surface deformation. The objective of this study is to investigate the torsional buckling behavior of commonly used materials and to develop a practical method for estimating the critical buckling rotation angle under physiological intramural vessel pressures. For this task, mechanical tests of four clinically approved materials, expanded polytetrafluoroethylene (ePTFE), Dacron, porcine and bovine pericardia, commonly used in pediatric cardiovascular surgeries, are conducted (n = 6). Torsional buckling initiation tests with n = 4 for the baseline case (L = 7.5 cm) and n = 3 for the validation of ePTFE (L = 15 cm) and Dacron (L = 15 cm and L = 25 cm) for each are also conducted at low venous pressures. A practical predictive formulation for the buckling potential is proposed using experimental observations and available theory. The relationship between the critical buckling rotation angle and the lumen pressure is determined by balancing the circumferential component of the compressive principal stress with the shear stress generated by the modified critical buckling torque, where the modified critical buckling torque depends linearly on the lumen pressure. While the proposed technique successfully predicted the critical rotation angle values lying within two standard deviations of the mean in the baseline case for all four materials at all lumen pressures, it could reliably predict the critical buckling rotation angles for ePTFE and Dacron samples of length 15 cm with maximum relative errors of 31% and 38%, respectively, in the validation phase. However, the validation of the performance of the technique demonstrated lower accuracy for Dacron samples of length 25 cm at higher pressure levels of 12 mmHg and 15 mmHg. Applicable to all surgical materials, this formulation enables surgeons to assess the torsional buckling potential of vascular conduits noninvasively. Bovine pericardium has been found to exhibit the highest stability, while Dacron (the lowest) and porcine pericardium have been identified as the least stable with the (unitless) torsional buckling resistance constants, 43,800, 12,300 and 14,000, respectively. There was no significant difference between ePTFE and Dacron, and between porcine and bovine pericardia. However, both porcine and bovine pericardia were found to be statistically different from ePTFE and Dacron individually (p < 0.0001). ePTFE exhibited highly nonlinear behavior across the entire strain range [0, 0.1] (or 10% elongation). The significant differences among the surgical materials reported here require special care in conduit construction and anastomosis design.
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    Patient-specific hemodynamics of new coronary artery bypass configurations
    (Springer, 2020) Rezaeimoghaddam, Mohammad; Oguz, Gokce Nur; Ates, Mehmet Sanser; Alkan Bozkaya, Tijen; Pişkin, Şenol; Lashkarinia, S Samaneh; Tenekecioglu, Erhan; Karagoz, Haldun; Pekkan, Kerem
    Purpose: This study aims to quantify the patient-specific hemodynamics of complex conduit routing configurations of coronary artery bypass grafting (CABG) operation which are specifically suitable for off-pump surgeries. Coronary perfusion efficacy and local hemodynamics of multiple left internal mammary artery (LIMA) with sequential and end-to-side anastomosis are investigated. Using a full anatomical model comprised of aortic arch and coronary artery branches the optimum perfusion configuration in multi-vessel coronary artery stenosis is desired. Methodology: Two clinically relevant CABG configurations are created using a virtual surgical planning tool where for each configuration set, the stenosis level, anastomosis distance and angle were varied. A non-Newtonian computational fluid dynamics solver in OpenFOAM incorporated with resistance boundary conditions representing the coronary perfusion physiology was developed. The numerical accuracy is verified and results agreed well with a validated commercial cardiovascular flow solver and experiments. For segmental performance analysis, new coronary perfusion indices to quantify deviation from the healthy scenario were introduced. Results: The first simulation configuration set;-a CABG targeting two stenos sites on the left anterior descending artery (LAD), the LIMA graft was capable of 31 mL/min blood supply for all the parametric cases and uphold the healthy LAD perfusion in agreement with the clinical experience. In the second end-to-side anastomosed graft configuration set;-the radial artery graft anastomosed to LIMA, a maximum of 64 mL/min flow rate in LIMA was observed. However, except LAD, the obtuse marginal (OM) and second marginal artery (m2) suffered poor perfusion. In the first set, average wall shear stress (WSS) were in the range of 4 to 35 dyns/cm2 for in LAD. Nevertheless, for second configuration sets the WSS values were higher as the LIMA could not supply enough blood to OM and m2. Conclusion: The virtual surgical configurations have the potential to improve the quality of operation by providing quantitative surgical insight. The degree of stenosis is a critical factor in terms of coronary perfusion and WSS. The sequential anastomosis can be done safely if the anastomosis angle is less than 90 degrees regardless of degree of stenosis. The smaller proposed perfusion index value, O(0.04 - 0) × 102, enable us to quantify the post-op hemodynamic performance by comparing with the ideal healthy physiological flow.
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    Review of machine learning techniques in soft tissue biomechanics and biomaterials
    (Springer, 2024) Donmazov, Samir; Saruhan, Eda Nur; Pekkan, Kerem; Pişkin, Şenol
    Background and ObjectiveAdvanced material models and material characterization of soft biological tissues play an essential role in pre-surgical planning for vascular surgeries and transcatheter interventions. Recent advances in heart valve engineering, medical device and patch design are built upon these models. Furthermore, understanding vascular growth and remodeling in native and tissue-engineered vascular biomaterials, as well as designing and testing drugs on soft tissue, are crucial aspects of predictive regenerative medicine. Traditional nonlinear optimization methods and finite element (FE) simulations have served as biomaterial characterization tools combined with soft tissue mechanics and tensile testing for decades. However, results obtained through nonlinear optimization methods are reliable only to a certain extent due to mathematical limitations, and FE simulations may require substantial computing time and resources, which might not be justified for patient-specific simulations. To a significant extent, machine learning (ML) techniques have gained increasing prominence in the field of soft tissue mechanics in recent years, offering notable advantages over conventional methods. This review article presents an in-depth examination of emerging ML algorithms utilized for estimating the mechanical characteristics of soft biological tissues and biomaterials. These algorithms are employed to analyze crucial properties such as stress-strain curves and pressure-volume loops. The focus of the review is on applications in cardiovascular engineering, and the fundamental mathematical basis of each approach is also discussed.MethodsThe review effort employed two strategies. First, the recent studies of major research groups actively engaged in cardiovascular soft tissue mechanics are compiled, and research papers utilizing ML and deep learning (DL) techniques were included in our review. The second strategy involved a standard keyword search across major databases. This approach provided 11 relevant ML articles, meticulously selected from reputable sources including ScienceDirect, Springer, PubMed, and Google Scholar. The selection process involved using specific keywords such as "machine learning" or "deep learning" in conjunction with "soft biological tissues", "cardiovascular", "patient-specific," "strain energy", "vascular" or "biomaterials". Initially, a total of 25 articles were selected. However, 14 of these articles were excluded as they did not align with the criteria of focusing on biomaterials specifically employed for soft tissue repair and regeneration. As a result, the remaining 11 articles were categorized based on the ML techniques employed and the training data utilized.ResultsML techniques utilized for assessing the mechanical characteristics of soft biological tissues and biomaterials are broadly classified into two categories: standard ML algorithms and physics-informed ML algorithms. The standard ML models are then organized based on their tasks, being grouped into Regression and Classification subcategories. Within these categories, studies employ various supervised learning models, including support vector machines (SVMs), bagged decision trees (BDTs), artificial neural networks (ANNs) or deep neural networks (DNNs), and convolutional neural networks (CNNs). Additionally, the utilization of unsupervised learning approaches, such as autoencoders incorporating principal component analysis (PCA) and/or low-rank approximation (LRA), is based on the specific characteristics of the training data. The training data predominantly consists of three types: experimental mechanical data, including uniaxial or biaxial stress-strain data; synthetic mechanical data generated through non-linear fitting and/or FE simulations; and image data such as 3D second harmonic generation (SHG) images or computed tomography (CT) images. The evaluation of performance for physics-informed ML models primarily relies on the coefficient of determination R2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${R}<^>{2}$$\end{document}. In contrast, various metrics and error measures are utilized to assess the performance of standard ML models. Furthermore, our review includes an extensive examination of prevalent biomaterial models that can serve as physical laws for physics-informed ML models.ConclusionML models offer an accurate, fast, and reliable approach for evaluating the mechanical characteristics of diseased soft tissue segments and selecting optimal biomaterials for time-critical soft tissue surgeries. Among the various ML models examined in this review, physics-informed neural network models exhibit the capability to forecast the mechanical response of soft biological tissues accurately, even with limited training samples. These models achieve high R2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${R}<^>{2}$$\end{document} values ranging from 0.90 to 1.00. This is particularly significant considering the challenges associated with obtaining a large number of living tissue samples for experimental purposes, which can be time-consuming and impractical. Additionally, the review not only discusses the advantages identified in the current literature but also sheds light on the limitations and offers insights into future perspectives.