Computational Pre-surgical Planning of Arterial Patch Reconstruction: Parametric Limits and In Vitro Validation

Review of Machine Learning Techniques in Soft Tissue Biomechanics and Biomaterials

BACKGROUND AND OBJECTIVE

Advanced 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.

METHODS

The 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.

RESULTS

ML 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 determinationR 2 . 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.

CONCLUSION

ML 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 highR 2 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.

Mechanical characterization and torsional buckling of pediatric cardiovascular materials

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.

A Systematic Review of the Application of Computational Technology in Microtia

Microtia is a congenital and morphological anomaly of one or both ears, which results from a confluence of genetic and external environmental factors. Up to now, extensive research has explored the potential utilization of computational methodologies in microtia and has obtained promising results. Thus, the authors reviewed the achievements and shortcomings of the research mentioned previously, from the aspects of artificial intelligence, computer-aided design and surgery, computed tomography, medical and biological data mining, and reality-related technology, including virtual reality and augmented reality. Hoping to offer novel concepts and inspire further studies within this field.

Surgical Patching in Congenital Heart Disease: The Role of Imaging and Modelling

In congenital heart disease, patches are not tailored to patient-specific anatomies, leading to shape mismatch with likely functional implications. The design of patches through imaging and modelling may be beneficial, as it could improve clinical outcomes and reduce the costs associated with redo procedures. Whilst attention has been paid to the material of the patches used in congenital surgery, this review outlines the current knowledge on this subject and isolated experimental work that uses modelling and imaging-derived information (including 3D printing) to inform the design of the surgical patch.

Clinical situations for which 3D Printing is considered an appropriate representation or extension of data contained in a medical imaging examination: vascular conditions

BACKGROUND

Medical three-dimensional (3D) printing has demonstrated utility and value in anatomic models for vascular conditions. A writing group composed of the Radiological Society of North America (RSNA) Special Interest Group on 3D Printing (3DPSIG) provides appropriateness recommendations for vascular 3D printing indications.

METHODS

A structured literature search was conducted to identify all relevant articles using 3D printing technology associated with vascular indications. Each study was vetted by the authors and strength of evidence was assessed according to published appropriateness ratings.

RESULTS

Evidence-based recommendations for when 3D printing is appropriate are provided for the following areas: aneurysm, dissection, extremity vascular disease, other arterial diseases, acute venous thromboembolic disease, venous disorders, lymphedema, congenital vascular malformations, vascular trauma, vascular tumors, visceral vasculature for surgical planning, dialysis access, vascular research/development and modeling, and other vasculopathy. Recommendations are provided in accordance with strength of evidence of publications corresponding to each vascular condition combined with expert opinion from members of the 3DPSIG.

CONCLUSION

This consensus appropriateness ratings document, created by the members of the 3DPSIG, provides an updated reference for clinical standards of 3D printing for the care of patients with vascular conditions.

Mid-term results following pulmonary artery patch augmentation in congenital heart disease

Background

Treatment of pulmonary artery (PA) stenosis in congenital heart disease is associated with adverse outcomes. The aim of this retrospective cohort study was to compare outcomes after surgical patch augmentation of PA stenosis in patients with biventricular congenital heart disease using different patch materials.

Methods

We identified all patients from our institutional congenital heart disease database who underwent patch augmentation for PA stenosis on the main pulmonary artery (MPA) or PA branches between 2012 and 2018. Patch materials used were glutaraldehyde fixated autologous pericardium (AP), expanded polytetrafluoroethylene (ePTFE), equine pericardium (EP), and bovine pericardium (BP). The primary study endpoint was the composite of catheter-based re-intervention or re-operation to relieve recurrent stenosis at the site of prior implanted patch material.

Results

A total of 156 patients (median age, 5 months, range, 0-85 months; median weight, 6.2 kg, range, 2.8-15.0 kg) underwent patch augmentation using 163 patches (ePTFE =99, 61%; EP =34, 21%; AP =25, 15%; BP =5, 3%). Overall, 131 (84%) patients underwent patch augmentation at the MPA, and 25 (16%) patients underwent patch augmentation at one or both PA branches. Over a mean follow-up period of 4±2 years, 30 patients (19%) reached the study endpoint. Freedom from primary endpoint was 92%±3% for the MPA and 25%±9% for PA branches at 5 years, respectively (P<0.001). Comparison of patch materials revealed similar re-intervention rates between ePTFE, AP, and EP. In contrast, outcomes were significantly decreased following the usage of BP when compared to other materials (ePTFE vs. BP, P=0.01; EP vs. BP, P=0.005). In the multivariable analysis, lower weight at index operation, patch augmentation of PA branches, and usage of BP were independently associated with re-intervention.

Conclusions

Patch augmentation of the MPA was associated with acceptable outcomes, while patch augmentation of PA branch stenosis remained independently associated with re-intervention. None of the used patch materials demonstrated superiority; however, BP had a higher rate of re-interventions.

Recasting Current Knowledge of Human Fetal Circulation: The Importance of Computational Models

Computational hemodynamic simulations are becoming increasingly important for cardiovascular research and clinical practice, yet incorporating numerical simulations of human fetal circulation is relatively underutilized and underdeveloped. The fetus possesses unique vascular shunts to appropriately distribute oxygen and nutrients acquired from the placenta, adding complexity and adaptability to blood flow patterns within the fetal vascular network. Perturbations to fetal circulation compromise fetal growth and trigger the abnormal cardiovascular remodeling that underlies congenital heart defects. Computational modeling can be used to elucidate complex blood flow patterns in the fetal circulatory system for normal versus abnormal development. We present an overview of fetal cardiovascular physiology and its evolution from being investigated with invasive experiments and primitive imaging techniques to advanced imaging (4D MRI and ultrasound) and computational modeling. We introduce the theoretical backgrounds of both lumped-parameter networks and three-dimensional computational fluid dynamic simulations of the cardiovascular system. We subsequently summarize existing modeling studies of human fetal circulation along with their limitations and challenges. Finally, we highlight opportunities for improved fetal circulation models.

A Comparison of Vessel Patch Materials in Tetralogy of Fallot Patients Using Virtual Surgery Techniques

Tetralogy of Fallot (ToF) is characterized by stenosis causing partial obstruction of the right ventricular outflow tract, typically alleviated through the surgical application of a vessel patch made from a biocompatible material. In this study, we use computational simulations to compare the mechanical performance of four patch materials for various stenosis locations. Nine idealized pre-operative ToF geometries were created by imposing symmetrical stenoses on each of three anatomical sub-regions of the pulmonary arteries of three patients with previously repaired ToF. A virtual surgery methodology was implemented to replicate the steps of vessel de-pressurization, surgical patching, and subsequent vessel expansion after reperfusion. Significant differences in patch average stress (p < 0.001) were found between patch materials. Biological patch materials (porcine xenopericardium, human pericardium) exhibited higher patch stresses in comparison to synthetic patch materials (Dacron and PTFE). Observed differences were consistent across the various stenosis locations and were insensitive to patient anatomy.

Computational Analysis of the Pulmonary Arteries in Congenital Heart Disease: A Review of the Methods and Results

With the help of computational fluid dynamics (CFD), hemodynamics of the pulmonary arteries (PA's) can be studied in detail and varying physiological circumstances and treatment options can be simulated. This offers the opportunity to improve the diagnostics and treatment of PA stenosis in biventricular congenital heart disease (CHD). The aim of this review was to evaluate the methods of computational studies for PA's in biventricular CHD and the level of validation of the numerical outcomes. A total of 34 original research papers were selected. The literature showed a great variety in the used methods for (re) construction of the geometry as well as definition of the boundary conditions and numerical setup. There were 10 different methods identified to define inlet boundary conditions and 17 for outlet boundary conditions. A total of nine papers verified their CFD outcomes by comparing results to clinical data or by an experimental mock loop. The diversity in used methods and the low level of validation of the outcomes result in uncertainties regarding the reliability of numerical studies. This limits the current clinical utility of CFD for the study of PA flow in CHD. Standardization and validation of the methods are therefore recommended.

Computational modeling of vascular growth in patient-specific pulmonary arterial patch reconstructions

Recent progress in vascular growth mechanics has involved the use of computational algorithms to address clinical problems with the use of three-dimensional patient specific geometries. The objective of this study is to establish a predictive computational model for the volumetric growth of pulmonary arterial (PA) tissue following complex cardiovascular patch reconstructive surgeries for congenital heart disease patients. For the first time in the literature, the growth mechanics and performance of artificial cardiovascular 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 surgical patch reconstruction process for an idealized main PA anatomy as a benchmark model and then for the patient-specific PA of a newborn. Following patch reconstruction, high levels of stress and strain are compensated by growth on the arterial tissue. As this growth progresses, the arterial tissue is predicted to stiffen to limit elastic deformations. We simulated this arterial growth up to the age of 18 years, when somatic growth plateaus. Our research findings show that the non-growing patch material remains in a 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 blood pressure, 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 to quantify the unbalanced growth of native arteries and artificial non-growing materials to develop optimal patch biomechanics for improved postoperative outcomes.

A canonical correlation analysis of the relationship between clinical attributes and patient-specific hemodynamic indices in adult pulmonary hypertension

Pulmonary hypertension (PH) is a progressive disease affecting approximately 10-52 cases per million, with a higher incidence in women, and with a high mortality associated with right ventricle (RV) failure. In this work, we explore the relationship between hemodynamic indices, calculated from in silico models of the pulmonary circulation, and clinical attributes of RV workload and pathological traits. Thirty-four patient-specific pulmonary arterial tree geometries were reconstructed from computed tomography angiography images and used for volume meshing for subsequent computational fluid dynamics (CFD) simulations. Data obtained from the CFD simulations were post-processed resulting in hemodynamic indices representative of the blood flow dynamics. A retrospective review of medical records was performed to collect the clinical variables measured or calculated from standard hospital examinations. Statistical analyses and canonical correlation analysis (CCA) were performed for the clinical variables and hemodynamic indices. Systolic pulmonary artery pressure (sPAP), diastolic pulmonary artery pressure (dPAP), cardiac output (CO), and stroke volume (SV) were moderately correlated with spatially averaged wall shear stress (0.60 ≤ R² ≤ 0.66; p < 0.05). Similarly, the CCA revealed a linear and strong relationship (ρ = 0.87; p << 0.001) between 5 clinical variables and 2 hemodynamic indices. To this end, in silico models of PH blood flow dynamics have a high potential for predicting the relevant clinical attributes of PH if analyzed in a group-wise manner using CCA.

Generation of a Purified iPSC-Derived Smooth Muscle-like Population for Cell Sheet Engineering

Induced pluripotent stem cells (iPSCs) provide a potential source for the derivation of smooth muscle cells (SMCs); however, current approaches are limited by the production of heterogeneous cell types and a paucity of tools or markers for tracking and purifying candidate SMCs. Here, we develop murine and human iPSC lines carrying fluorochrome reporters (Acta2hrGFP and ACTA2eGFP, respectively) that identify Acta2+/ACTA2+ cells as they emerge in vitro in real time during iPSC-directed differentiation. We find that Acta2hrGFP+ and ACTA2eGFP+ cells can be sorted to purity and are enriched in markers characteristic of an immature or synthetic SMC. We characterize the resulting GFP+ populations through global transcriptomic profiling and functional studies, including the capacity to form engineered cell sheets. We conclude that these reporter lines allow for generation of sortable, live iPSC-derived Acta2+/ACTA2+ cells highly enriched in smooth muscle lineages for basic developmental studies, tissue engineering, or future clinical regenerative applications.

Radiological Society of North America (RSNA) 3D printing Special Interest Group (SIG): guidelines for medical 3D printing and appropriateness for clinical scenarios

Medical three-dimensional (3D) printing has expanded dramatically over the past three decades with growth in both facility adoption and the variety of medical applications. Consideration for each step required to create accurate 3D printed models from medical imaging data impacts patient care and management. In this paper, a writing group representing the Radiological Society of North America Special Interest Group on 3D Printing (SIG) provides recommendations that have been vetted and voted on by the SIG active membership. This body of work includes appropriate clinical use of anatomic models 3D printed for diagnostic use in the care of patients with specific medical conditions. The recommendations provide guidance for approaches and tools in medical 3D printing, from image acquisition, segmentation of the desired anatomy intended for 3D printing, creation of a 3D-printable model, and post-processing of 3D printed anatomic models for patient care.