Biology subjects

Kuhl, E.

Publications and source records attributed to Kuhl, E..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Mimicking mechanics: A comparison of meat and meat analogs

The texture of meat is one of the most important features to mimic when developing meat analogs. Both protein source and processing method impact the texture of the final product. We can distinguish three types of mechanical tests to quantify the textural differences between meat and meat analogs: puncture-type, rheological torsion tests, and classical mechanical tests of tension, compression, and bending. Here we compile the shear force and stiffness values of whole and comminuted meats and meat analogs from the two most popular tests for meat, the Warner-Bratzler shear test and the double-compression texture profile analysis. Our results suggest that, with the right fine-tuning, todays meat analogs are well capable of mimicking the mechanics of real meat. While Warner-Bratzler shear tests and texture profile analysis provide valuable information about the tenderness and sensory perception of meat, both tests suffer from a lack of standardization, which limits cross-study comparisons. Here we provide guidelines to standardize meat testing and report the meat stiffness as the single most informative mechanical parameter. Collecting big standardized data and sharing them with the community at large could empower researchers to harness the power of generative artificial intelligence to inform the systematic development of meat analogs with desired mechanical properties and functions, taste and sensory perception.

bioengineering↗

bioRxiv2024

Discovering uncertainty: Bayesian constitutive artificial neural networks

Understanding uncertainty is critical, especially when data are sparse and variations are large. Bayesian neural networks offer a powerful strategy to build predictable models from sparse data, and inherently quantify both, aleatoric uncertainties of the data and epistemic uncertainties of the model. Yet, classical Bayesian neural networks ignore the fundamental laws of physics, they are non-interpretable, and their parameters have no physical meaning. Here we integrate concepts of Bayesian learning and constitutive neural networks to discover interpretable models, parameters, and uncertainties that best explain soft matter systems. Instead of training an individual constitutive neural network and learning point values of the network weights, we train an ensemble of networks and learn probability distributions of the weights, along with their means, standard deviations, and credible intervals. We use variational Bayesian inference and adopt an efficient backpropagation-compatible algorithm that approximates the true probability distributions by simpler distributions and minimizes their divergence through variational learning. When trained on synthetic data, our Bayesian constitutive neural network successfully rediscovers the initial model, even in the presence of noise, and robustly discovers uncertainties, even from incomplete data. When trained on real data from healthy and aneurysmal human arteries, our network discovers a model with more stretch stiffening, more anisotropy, and more uncertainty for diseased than for healthy arteries. Our results demonstrate that Bayesian constitutive neural networks can successfully discriminate between healthy and diseased arteries, robustly discover interpretable models and parameters for both, and efficiently quantify uncertainties in model discovery. We anticipate our approach to generalize to other soft biomedical systems for which real-world data are rare and inter-personal variations are large. Ultimately, our calculated uncertainties will help enhance model robustness, promote personalized predictions, enable informed decision-making, and build confidence in automated model discovery and simulation. Our source code, data, and examples are available at https://github.com/LivingMatterLab/CANN.

bioengineering↗

bioRxiv2024

Automated model discovery for textile structures:The unique mechanical signature of warp knitted fabrics

Textile fabrics have unique mechanical properties, which make them ideal candidates for many engineering and medical applications: They are initially flexible, nonlinearly stiffening, and ultra-anisotropic. Various studies have characterized the response of textile structures to mechanical loading; yet, our understanding of their exceptional properties and functions remains incomplete. Here we integrate biaxial testing and constitutive neural networks to automatically discover the best model and parameters to characterize warp knitted polypropylene fabrics. We use experiments from different mounting orientations, and discover interpretable anisotropic models that perform well during both training and testing. Our study shows that constitutive models for warp knitted fabrics are highly sensitive to an accurate representation of the textile microstructure, and that models with three microstructural directions outperform classical orthotropic models with only two in-plane directions. Strikingly, out of 214 =16,384 possible combinations of terms, we consistently discover models with two exponential linear fourth invariant terms that inherently capture the initial flexibility of the virgin mesh and the pronounced nonlinear stiffening as the loops of the mesh tighten. We anticipate that the tools we have developed and prototyped here will generalize naturally to other textile fabrics-woven or knitted, weft knit or warp knit, polymeric or metallic-and, ultimately, will enable the robust discovery of anisotropic constitutive models for a wide variety of textile structures. Beyond discovering constitutive models, we envision to exploit automated model discovery as a novel strategy for the generative material design of wearable devices, stretchable electronics, and smart fabrics, as programmable textile metamaterials with tunable properties and functions. Our source code, data, and examples are available at https://github.com/LivingMatterLab/CANN.

bioengineering↗

bioRxiv2024

Two for tau: Automated model discovery reveals two-stage tau aggregation dynamics in Alzheimer's disease

Alzheimers disease is a neurodegenerative disorder characterized by the presence of amyloid-{beta} plaques and the accumulation of misfolded tau proteins and neurofibrillary tangles in the brain. A thorough understanding of the local accumulation of tau is critical to develop effective therapeutic strategies. Tau pathology has traditionally been described using reaction-diffusion models, which succeed in capturing the global spread, but fail to accurately describe the local aggregation dynamics. Current mathematical models enforce a single-peak behavior in tau aggregation, which does not align well with clinical observations. Here we identify a more accurate description of tau aggregation that reflects the complex patterns observed in patients. We propose an innovative approach that uses constitutive neural networks to autonomously discover bell-shaped aggregation functions with multiple peaks from clinical positron emission tomography (PET) data of misfolded tau protein. Our method reveals previously overlooked two-stage aggregation dynamics by uncovering a twoterm ordinary differential equation that links the local accumulation rate to the tau concentration. When trained on data from amyloid-{beta} positive and negative subjects, the neural network clearly distinguishes between both groups and uncovers a more subtle relationship between amyloid-{beta} and tau than previously postulated. In line with the amyloid-tau dual pathway hypothesis, our results show that the presence of toxic amyloid-{beta} influences the accumulation of tau, particularly in the earlier disease stages. We expect that our approach to autonomously discover the accumulation dynamics of pathological proteins will improve simulations of tau dynamics in Alzheimers disease and provide new insights into disease progression.

biophysics↗

bioRxiv2024

Personalizing a computational upper body model improves kinematic tracking in high range-of-motion arm movements

Musculoskeletal models of the shoulder are needed to understand the mechanics of overhead motions. Existing models can be scaled to represent the size of an individual person, but the kinematics are generic. We introduce a method to personalize the shoulder kinematics of a computational model of the upper body that defines the orientations of the clavicle and scapula based on glenohumeral joint angles. During five static calibration poses, we palpate and measure the orientation of the scapula. We explore the importance of representing shoulder elevation by introducing clavicle elevation as a degree of freedom that is independent of the glenohumeral angles. For ten subjects, we record the five calibration poses, ten additional static poses, and dynamic arm raises using optical motion capture. We examine the data using a dynamically-constrained inverse kinematics analysis. Personalization, independent clavicle elevation, and both in combination reduce the average upper body marker tracking error compared to the generic model in the static poses (26 mm to 17-20 mm) and in the dynamic trials (22 mm to 14-17 mm). Only personalization reduces the average scapula marker error (51 mm to 36-38 mm) and scapula axis-angle error (15{degrees} to 10{degrees}) in the static poses, and in the dynamic trials at instances that best match the static poses (53 mm to 37-40 mm, 15{degrees} to 9{degrees}). Our results show that personalizing upper body models improves kinematic tracking. We provide our experimental data, model, and methods to allow researchers to reproduce and build upon our results.

bioengineering↗

bioRxiv2024

Got meat? The mechanical signature of plant-based and animal meat

Eating less meat is associated with a healthier body and planet. Yet, we remain reluctant to switch to a plant-based diet, largely due to the sensory experience of plant-based meat. Food scientists characterize meat using a double compression test, which only probes one-dimensional behavior. Here we use tension, compression, and shear tests-combined with constitutive neural networks-to automatically discover the behavior of eight plant-based and animal meats across the entire three-dimensional spectrum. We find that plant-based sausage and hotdog, with stiffnesses of 95.9{+/-}14.1kPa and 38.7{+/-}3.0kPa, successfully mimic their animal counterparts, with 63.5{+/-}45.7kPa and 44.3{+/-}13.2 kPa, while tofurky is twice as stiff, and tofu is twice as soft. Strikingly, a complementary food tasting survey produces in nearly identical stiffness rankings for all eight products ({rho} =0.833, p=0.015). Probing the fully three-dimensional signature of meats is critical to understand subtle differences in texture that may result in a different perception of taste. Our data and code are freely available at https://github.com/LivingMatterLab/CANN

bioengineering↗

bioRxiv2024

Automated model discovery for human cardiac tissue: Discovering the best model and parameters

For more than half a century, scientists have developed mathematical models to understand the behavior of the human heart. Today, we have dozens of heart tissue models to choose from, but selecting the best model is limited to expert professionals, prone to user bias, and vulnerable to human error. Here we take the human out of the loop and automate the process of model discovery. Towards this goal, we establish a novel incompressible orthotropic constitutive neural network to simultaneously discover both, model and parameters, that best explain human cardiac tissue. Notably, our network features 32 individual terms, 8 isotropic and 24 anisotropic, and fully autonomously selects the best model, out of more than 4 billion possible combinations of terms. We demonstrate that we can successfully train the network with triaxial shear and biaxial extension tests and systematically sparsify the parameter vector with L1-regularization. Strikingly, we robustly discover a four-term model that features a quadratic term in the second invariant I2, and exponential quadratic terms in the fourth and eighth invariants I4f, I4n, and I8fs. Importantly, our discovered model is interpretable by design and has parameters with well-defined physical units. We show that it outperforms popular existing myocardium models and generalizes well, from homogeneous laboratory tests to heterogeneous whole heart simulations. This is made possible by a new universal material subroutine that directly takes the discovered network weights as input. Automating the process of model discovery has the potential to democratize cardiac modeling, broaden participation in scientific discovery, and accelerate the development of innovative treatments for cardiovascular disease. Our source code, data, and examples are available at https://github.com/LivingMatterLab/CANN.

bioengineering↗

bioRxiv2023

Democratizing biomedical simulation through automated model discovery and a universal material subroutine

Personalized computational simulations have emerged as a vital tool to understand the biomechanical factors of a disease, predict disease progression, and design personalized intervention. Material modeling is critical for realistic biomedical simulations, and poor model selection can have life-threatening consequences for the patient. However, selecting the best model requires a profound domain knowledge and is limited to a few highly specialized experts in the field. Here we explore the feasibility of eliminating user involvement and automate the process of material modeling in finite element analyses. We leverage recent developments in constitutive neural networks, machine learning, and artificial intelligence to discover the best constitutive model from thousands of possible combinations of a few functional building blocks. We integrate all discoverable models into the finite element workflow by creating a universal material subroutine that contains more than 60,000 models, made up of 16 individual terms. We prototype this workflow using biaxial extension tests from healthy human arteries as input and stress and stretch profiles across the human aortic arch as output. Our results suggest that constitutive neural networks can robustly discover various flavors of arterial models from data, feed these models directly into a finite element simulation, and predict stress and strain profiles that compare favorably to the classical Holzapfel model. Replacing dozens of individual material subroutines by a single universal material subroutine-populated directly via automated model discovery-will make finite element simulations more user-friendly, more robust, and less vulnerable to human error. Democratizing finite element simulation by automating model selection could induce a paradigm shift in physics-based modeling, broaden access to simulation technologies, and empower individuals with varying levels of expertise and diverse backgrounds to actively participate in scientific discovery and push the boundaries of biomedical simulation.

bioengineering↗

bioRxiv2023

On automated model discovery and a universal material subroutine

Constitutive modeling is the cornerstone of computational and structural mechanics. In a finite element analysis, the constitutive model is encoded in the material subroutine, a function that maps local strains onto stresses. This function is called within every finite element, at each integration point, within every time step, at each Newton iteration. Todays finite element packages offer large libraries of material subroutines to choose from. However, the scientific criteria for model selection remain highly subjective and prone to user bias. Here we fully automate the process of model selection, autonomously discover the best model and parameters from experimental data, encode all possible discoverable models into a single material subroutine, and seamlessly integrate this universal material subroutine into a finite element analysis. We prototype this strategy for tension, compression, and shear data from human brain tissue and perform a hyperelastic model discovery from twelve possible terms. These terms feature the first and second invariants, raised to the first and second powers, embedded in the identity, exponential, and logarithmic functions, generating 22x2x3 = 4096 models in total. We demonstrate how to integrate these models into a single universal material subroutine that features the classical neo Hooke, Blatz Ko, Mooney Rivlin, Demiray, Gent, and Holzapfel models as special cases. Finite element simulations with our universal material subroutine show that it specializes well to these widely used models, generalizes well to newly discovered models, and agrees excellently with both experimental data and previous simulations. It also performs well within realistic finite element simulations and accurately predicts stress concentrations in the human brain for six different head impact scenarios. We anticipate that integrating automated model discovery into a universal material subroutine will generalize naturally to more complex anisotropic, compressible, and inelastic materials and to other nonlinear finite element platforms. Replacing dozens of individual material subroutines by a single universal material subroutine that is populated directly via automated model discovery--entirely without human interaction--makes finite element analyses more accessible, more robust, and less vulnerable to human error. This could forever change how we simulate materials and structures.

bioengineering↗

bioRxiv2023

Discovering the mechanics of artificial and real meat

Artificial meat is an eco-friendly alternative to real meat that is marketed to have a similar taste and feel. The mechanical properties of artificial meat significantly influence our perception of taste, but how precisely the mechanics of artificial meat compare to real meat remains insufficiently understood. Here we perform mechanical tension, compression, and shear tests on isotropic artificial meat (Tofurky(R) Plant-Based Deli Slices), anisotropic artificial meat (Daring Chickn Pieces) and anisotropic real meat (chicken) and analyze the data using constitutive neural networks and automated model discovery. Our study shows that, when deformed by 10%, artificial and real chicken display similar maximum stresses of 21.0 kPa and 21.8 kPa in tension, -7.2 kPa and -16.4 kPa in compression, and 2.4 kPa and 0.9 kPa in shear, while the maximum stresses for tofurky were 28.5 kPa, -38.3 kP, and 5.5 kPa. To discover the mechanics that best explain these data, we consulted two constitutive neural networks of Ogden and Valanis-Landel type. Both networks robustly discover models and parameters to explain the complex nonlinear behavior of artificial and real meat for individual tension, compression, and shear tests, and for all three tests combined. When constrained to the classical neo Hooke, Blatz Ko, and Mooney Rivlin models, both networks discover shear moduli of 94.4 kPa for tofurky, 35.7 kPa for artificial chickn, and 21.4 kPa for real chicken. Our results suggests that artificial chicken succeeds in re-producing the mechanical properties of real chicken across all loading modes, while tofurky does not, and is about three times stiffer. Strikingly, all three meat products display shear softening and their resistance to shear is about an order of magnitude lower than their resistance to tension and compression. We anticipate our study to inspire more quantitative, mechanistic comparisons of artificial and real meat. Our automated-model-discovery based approach has the potential to inform the design of more authentic meat substitutes with an improved perception of taste, with the ultimate goal to reduce environmental impact, improve animal welfare, and mitigate climate change, while still offering the familiar taste and texture of traditional meat. Our source code, data, and examples will be available at https://github.com/LivingMatterLab/CANNs.

bioengineering↗

bioRxiv2023

Automated model discovery for muscle using constitutive recurrent neural networks

The stiffness of soft biological tissues not only depends on the applied deformation, but also on the deformation rate. To model this type of behavior, traditional approaches select a specific time-dependent constitutive model and fit its parameters to experimental data. Instead, a new trend now suggests a machine-learning based approach that simultaneously discovers both the best model and best parameters to explain given data. Recent studies have shown that feed-forward constitutive neural networks can robustly discover constitutive models and parameters for hyperelastic materials. However, feed-forward architectures fail to capture the history dependence of viscoelastic soft tissues. Here we combine a feed-forward constitutive neural network for the hyperelastic response and a recurrent neural network for the viscous response inspired by the theory of quasi-linear viscoelasticity. Our novel rheologically-informed network architecture discovers the time-independent initial stress using the feed-forward network and the time-dependent relaxation using the recurrent network. We train and test our combined network using unconfined compression relaxation experiments of passive skeletal muscle and compare our discovered model to a neo Hookean standard linear solid and to a vanilla recurrent neural network with no mechanics knowledge. We demonstrate that, for limited experimental data, our new constitutive recurrent neural network discovers models and parameters that satisfy basic physical principles and generalize well to unseen data. We discover a Mooney-Rivlin type two-term initial stored energy function that is linear in the first invariant I1 and quadratic in the second invariant I2 with stiffness parameters of 0.60kPa and 0.55kPa. We also discover a Prony-series type relaxation function with time constants of 0.362s, 2.54s, and 52.0s with coefficients of 0.89, 0.05, and 0.03. Our newly discovered model outperforms both the neo Hookean standard linear solid and the vanilla recurrent neural network in terms of prediction accuracy on unseen data. Our results suggest that constitutive recurrent neural networks can autonomously discover both model and parameters that best explain experimental data of soft viscoelastic tissues. Our source code, data, and examples are available at https://github.com/LivingMatterLab.

bioengineering↗

bioRxiv2023

Principal-stretch-based constitutive neural networks autonomously discover a subclass of Ogden models for human brain tissue

The soft tissue of the brain deforms in response to external stimuli, which can lead to traumatic brain injury. Constitutive models relate the stress in the brain to its deformation and accurate constitutive modeling is critical in finite element simulations to estimate injury risk. Traditionally, researchers first choose a constitutive model and then fit the model parameters using tension, compression, or shear experiments. In contrast, constitutive artificial neural networks enable automated model discovery without having to choosing a specific model a priori before learning the model parameters. Here we reverse engineer a constitutive artificial neural network that uses the principal stretches, raised to a wide range of exponential powers, as activation functions for the hidden layer. Upon training, the network autonomously discovers a subclass of models with multiple Ogden terms that outperform popular constitutive models including neo Hooke, Blatz Ko, and Mooney Rivlin. While invariant-based networks fail to capture the pronounced tension-compression asymmetry of brain tissue, our principal-stretch-based network can simultaneously explain tension, compression, and shear data for the cortex, basal ganglia, corona radiata, and corpus callosum. Without fixing the number of terms a priori, our model self-selects the best subset of terms out of more than a million possible combinations, while simultaneously discovering the best model parameters and best experiment to train itself. Eliminating user-guided model selection has the potential to induce a paradigm shift in soft tissue modeling and democratize brain injury simulations. Our source code, data, and examples are available at https://github.com/LivingMatterLab/CANN.

biophysics↗

bioRxiv2022

Automated model discovery for skin:Discovering the best model, data, and experiment

Choosing the best constitutive model and the right set of model parameters is at the heart of continuum mechanics. For decades, the gold standard in constitutive modeling has been to first select a model and then fit its parameters to data. However, the success of this approach is highly dependent on user experience and personal preference. Here we propose a new method that simultaneously and fully autonomously discovers the best model and parameters to explain experimental data. Mathematically, the model finding is translated into a complex non-convex optimization problem. We solve this problem by formulating it as a neural network, and leveraging the success, robustness, and stability of the optimization tools developed in classical neural network modeling. Instead of using a classical off-the-shelf neural network, we design a new family of Constitutive Artificial Neural Networks with activation functions that feature popular constitutive models and parameters that have a clear physical interpretation. Our new network inherently satisfies general kinematic, thermodynamic, and physical constraints and trains robustly, even with sparse data. We illustrate its potential for biaxial extension experiments on skin and demonstrate that the majority of network weights train to zero, while the small subset of non-zero weights defines the discovered model. Unlike classical network weights, these weights are physically interpretable and translate naturally into engineering parameters and microstructural features such as stiffness and fiber orientation. Our results suggest that Constitutive Artificial Neural Networks enable automated model, parameter, and experiment discovery and could initiate a paradigm shift in constitutive modeling, from user-defined to automated model selection and parameterization. Our source code, data, and examples are available at https://github.com/LivingMatterLab/CANN.

bioengineering↗

bioRxiv2022

Automated model discovery for human brain using Constitutive Artificial Neural Networks

The brain is our softest and most vulnerable organ, and understanding its physics is a challenging but significant task. Massive efforts have been dedicated at testing the human brain, and various competing models have emerged to characterize its response to mechanical loading. However, selecting the best constitutive model remains a heuristic process that strongly depends on user experience and personal preference. Here we challenge the conventional wisdom to first select a constitutive model and then fit its parameters to experimental data. Instead, we propose a new strategy that simultaneously discovers both model and parameters that best describe the data. Towards this goal, we integrate more than a century of knowledge in thermodynamics and state-of-the-art machine learning to build a family of Constitutive Artificial Neural Networks that enable automated model discovery for human brain tissue. Our overall design paradigm is to reverse engineer a Constitutive Artificial Neural Network from a set of functional building blocks that are, by design, a generalization of widely used and commonly accepted constitutive models, including the neo Hooke, Blatz Ko, Mooney Rivlin, Demiray, Gent, and Holzapfel models. By constraining the input, output, activation functions, and architecture, our network a priori satisfies thermodynamic consistency, material objectivity, material symmetry, physical constrains, and polyconvexity. We demonstrate that our network autonomously discovers both model and parameters that best characterize the behavior of human gray and white matter under tension, compression, and shear. Importantly, our network weights translate naturally into physically meaningful material parameters, e.g., shear moduli of 1.82kPa, 0.88kPa, 0.94kPa, and 0.54kPa for the cortex, basal ganglia, corona radiata, and corpus callosum. Our results suggest that Constitutive Artificial Neural Networks have the potential to induce a paradigm shift in soft tissue modeling, from user-defined model selection to automated model discovery. Our source code, data, and examples are available at https://github.com/LivingMatterLab/CANN.

bioengineering↗

bioRxiv2022

Rheology of growing axons

The growth of axons is a key process in neural system development, which relies upon a subtle balance between external mechanical forces and remodeling of cellular constituents. A key problem in the biophysics of axons is therefore to understand the overall response of the axon under stretch, which is often modeled phenomenologically using morphoelastic or viscoelastic models. Here, we develop a microscopic mixture model of growth and remodeling based on protein turnover and damage to obtain the macroscopic rheology of axonal shafts. First, we provide an estimate for the instantaneous elastic response of axons. Second, we demonstrate that under moderate traction velocities, axons behave like a viscoelastic Maxwell material. Third, for larger velocities, we show that failure takes place due to extensive damage.

developmental biology↗

bioRxiv2021

Predicting brain atrophy from tau pathology: A summary of clinical findings and their translation into personalized models

For more than 25 years, the amyloid hypothesis-the paradigm that amyloid is the primary cause of Alzheimers disease-has dominated the Alzheimers community. Now, increasing evidence suggests that tissue atrophy and cognitive decline in Alzheimers disease are more closely linked to the amount and location of misfolded tau protein than to amyloid plaques. However, the precise correlation between tau pathology and tissue atrophy remains unknown. Here we integrate multiphysics modeling and Bayesian inference to create personalized tau-atrophy models using longitudinal clinical images from the the Alzheimers Disease Neuroimaging Initiative. For each subject, we infer three personalized parameters, the tau misfolding rate, the tau transport coefficient, and the tau-induced atrophy rate from four consecutive annual tau positron emission tomography scans and structural magnetic resonance images. Strikingly, the tau-induced atrophy coefficient of 0.13/year (95% CI: 0.097-0.189) was fairly consistent across all subjects suggesting a strong correlation between tau pathology and tissue atrophy. Our personalized whole brain atrophy rates of 0.68-1.68%/year (95% CI: 0.5-2.0) are elevated compared to healthy subjects and agree well with the atrophy rates of 1-3%/year reported for Alzheimers patients in the literature. Once comprehensively calibrated with a larger set of longitudinal images, our model has the potential to serve as a diagnostic and predictive tool to estimate future atrophy progression from clinical tau images on a personalized basis.

biophysics↗

bioRxiv2021

How drugs modulate the performance of the human heart

Many drugs interact with ion channels in the cells of the heart and trigger heart rhythm disorders with potentially fatal consequences. Computational modeling can provide mechanistic insight into the onset and propagation of drug-induced arrhythmias, but the effect of drugs on the mechanical behavior of the heart remains poorly understood. Here we establish a multiphysics framework that integrates the biochemical, electrical, and mechanical effects of drugs from single cardiac cells to the overall response of the whole heart. For the example of the drug dofetilide, we show that drug concentrations of 3.0x and 4.8x increase the heart rate to 122 and 114 beats per minute, increase the myofiber stretches up to 10%, and decrease tissue relaxation by 6%. Strikingly, the drug-induced interventricular and atrial-ventricular dyssynchrony results in a 2.5% decreased and 7% increased cardiac output, respectively. Our results demonstrate the potential for multiphysics, multiscale modeling towards understanding the mechanical implications of drug-induced arrhythmias. Knowing how differing drug concentrations affect the performance of the heart has important clinical implications in drug safety evaluation and personalized medicine.

biophysics↗

bioRxiv2021

A Framework for Evaluating Myocardial Stiffness Using 3D-Printed Hearts

MRI-driven computational modeling is increasingly used to simulate in vivo cardiac mechanical behavior and estimate subject-specific myocardial stiffness. However, in vivo validation of these estimates is exceedingly difficult due to the lack of a known ground-truth in vivo myocardial stiffness. We have developed 3D-printed heart phantoms of known myocardium-mimicking stiffness and MRI relaxation properties and incorporated the heart phantoms within a highly controlled MRI-compatible setup to simulate in vivo diastolic filling. The setup enables the acquisition of experimental data needed to evaluate myocardial stiffness using computational constitutive modeling: phantom geometry, loading pressures, boundary conditions, and filling strains. The pressure-volume relationship obtained from the phantom setup was used to calibrate an in silico model of the heart phantom undergoing simulated diastolic filling. The model estimated stiffness was compared with ground-truth stiffness obtained from uniaxial tensile testing. Ultimately, the setup is designed to enable extensive validation of MRI and FEM-based myocardial stiffness estimation frameworks.

bioengineering↗