Understanding regional mechanics of rat myocardia by fitting hyperelastic constitutive models

Abstract Background: Availability of biaxial mechanical data for heart myocardia remains high in demand for the development of accurate and detailed computational models. Lack of accurate mechanical data for myocardia may delay the understanding of heart diseases mechanisms. Therefore, the aim of this study is to develop understanding of the regional difference of wall mechanics using rat heart in the left ventricle (LV), septal wall (STW) and right ventricle (RV). This was achieved by conducting a biaxial test on three rat heart myocardia (i.e LV, RV and STW). To select the best hyperelastic model that may be utilised for the development of computational models of the heart, the Fung, Choi-Vito, Polynomial (Anisotropic), Four-Fiber family, Holzapfel (2000) and Holzapfel (2005) hyperelastic models were selected and fitted on the biaxial data of the LV, RV and STW. Results: The best hyperelastic model was selected based on evaluation index (EI) which utilises the Coefficient of Determination (R2). All the six hyperelastic constitutive models were then compared in all three rat heart myocardia. The results show that the Polynomial (Anisotropic) model outperforms the other five models in all myocardial tissues with EI’s above 90 %. The Four-fiber-family and the two Holzapfel models perform equally in the LV and STW myocardial tissue with EI of 50 and 70 %, respectively. Conclusions: The Fung and Choi-Vito models yielded poor goodness of fit in the LV and STW myocardial tissues. The results presented here will be useful for detailed development of accurate computational models studying mechanisms of cardiovascular diseases.

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Understanding regional mechanics of rat myocardia by fitting hyperelatsic models

10.21203/rs.3.rs-957393/v1 ◽

2021 ◽

Author(s):

Fulufhelo Nemavhola ◽

Harry Ngwangwa ◽

Thanyani Pandelani ◽

Neil Davies ◽

Thomas Franz

Keyword(s):

Rat Heart ◽

Goodness Of Fit ◽

Regional Difference ◽

Computational Models ◽

Coefficient Of Determination ◽

Myocardial Tissue ◽

Biaxial Test ◽

Anisotropic Model ◽

Septal Wall ◽

Hyperelastic Model

Abstract Availability of biaxial mechanical data for heart myocardia remains high in demand for the development of accurate and detailed computational models. The aim of this study is to study the regional difference of wall mechanics using rat heart in the left ventricle (LV), septal wall (STW) and right ventricle (RV). This was achieved by conducting a biaxial test on three rat heart myocardia (i.e LV, RV and STW). Fung, Choi-Vito, Polynomial (Anistropic), Four-Fiber family, Holzapfel (2000) and Holzapfel (2005) hyperelastic models were selected and fitted on the bixial data of the LV, RV and STW. The best hyperelastic model was the selected based on evaluation index (EI) determined from the coefficient of determination (R2). All the six models were then compared in all three rat heart myocardia. The results show that the Polynomial (Anisotropic) model outperforms the other five models in all myocardial tissues with EI’s above 90 % goodness of fit. The Four-fiber-family and the two Holzapfel models perform equally in the LV and STW myocardial tissue between 50 and 70 % goodness of fit. The Fung and Choi-Vito models yielded poor goodness of fit in the LV and STW myocardial tissues. Parameter fitting is useful method in advancing reliable data to be used in the development of accurate computational models.

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Study of biaxial mechanical properties of the passive pig heart: material characterisation and categorisation of regional differences

International Journal of Mechanical and Materials Engineering ◽

10.1186/s40712-021-00128-4 ◽

2021 ◽

Vol 16 (1) ◽

Author(s):

Fulufhelo Nemavhola

Keyword(s):

Mechanical Properties ◽

Constitutive Model ◽

Computational Models ◽

Heart Diseases ◽

Interventricular Septum ◽

Model Material ◽

Biaxial Test ◽

Material Behaviour ◽

Material Parameters ◽

Biaxial Tests

AbstractRegional mechanics of the heart is vital in the development of accurate computational models for the pursuit of relevant therapies. Challenges related to heart dysfunctioning are the most important sources of mortality in the world. For example, myocardial infarction (MI) is the foremost killer in sub-Saharan African countries. Mechanical characterisation plays an important role in achieving accurate material behaviour. Material behaviour and constitutive modelling are essential for accurate development of computational models. The biaxial test data was utilised to generated Fung constitutive model material parameters of specific region of the pig myocardium. Also, Choi-Vito constitutive model material parameters were also determined in various myocardia regions. In most cases previously, the mechanical properties of the heart myocardium were assumed to be homogeneous. Most of the computational models developed have assumed that the all three heart regions exhibit similar mechanical properties. Hence, the main objective of this paper is to determine the mechanical material properties of healthy porcine myocardium in three regions, namely left ventricle (LV), mid-wall/interventricular septum (MDW) and right ventricle (RV). The biomechanical properties of the pig heart RV, LV and MDW were characterised using biaxial testing. The biaxial tests show the pig heart myocardium behaves non-linearly, heterogeneously and anisotropically. In this study, it was shown that RV, LV and MDW may exhibit slightly different mechanical properties. Material parameters of two selected constitutive models here may be helpful in regional tissue mechanics, especially for the understanding of various heart diseases and development of new therapies.

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Passive Biaxial Tensile Dataset of Three Main Rat Heart Myocardia: Left Ventricle, Mid-Wall and Right Ventricle

10.20944/preprints202108.0153.v1 ◽

2021 ◽

Author(s):

Fulufhelo Nemavhola ◽

Harry Ngwangwa ◽

Neil Davies ◽

Thoams Franz

Keyword(s):

Left Ventricle ◽

Right Ventricle ◽

Rat Heart ◽

Computational Models ◽

Heart Diseases ◽

Tissue Mechanics ◽

Soft Tissue Mechanics ◽

Biaxial Tensile ◽

As Stress

This article presents raw data of biaxial tensile measurements of rat heart passive myocardium conducted in lab scale environment. The passive myocardium of the rat was divided into three regions, namely: left ventricle, mid-wall and right ventricle. The biaxial dataset of passive rat myocardia is presented as stress vs strain of the passive rat myocardium in various regions. The determination of valid material properties of the heart plays an important role in the development computational models. These computational models are useful in studying various scenarios and mechanisms of heart diseases. In addition, valid and accurate materials are critical in the development of new therapies. The dataset presented here is useful in the area of soft tissue mechanics including studying the mechanisms of heart diseases such as myocardial infarction. Accordingly, the evaluation of stress and strain in left ventricle, mid-wall and right ventricle was performed.

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Evaluation of NASA POWER Reanalysis Products to Estimate Daily Weather Variables in a Hot Summer Mediterranean Climate

Agronomy ◽

10.3390/agronomy11061207 ◽

2021 ◽

Vol 11 (6) ◽

pp. 1207

Author(s):

Gonçalo C. Rodrigues ◽

Ricardo P. Braga

Keyword(s):

Wind Speed ◽

Bias Correction ◽

Goodness Of Fit ◽

Mediterranean Climate ◽

Weather Data ◽

Coefficient Of Determination ◽

Mean Bias Error ◽

Bias Error ◽

Weather Variables ◽

Weather Stations

This study aims to evaluate NASA POWER reanalysis products for daily surface maximum (Tmax) and minimum (Tmin) temperatures, solar radiation (Rs), relative humidity (RH) and wind speed (Ws) when compared with observed data from 14 distributed weather stations across Alentejo Region, Southern Portugal, with a hot summer Mediterranean climate. Results showed that there is good agreement between NASA POWER reanalysis and observed data for all parameters, except for wind speed, with coefficient of determination (R2) higher than 0.82, with normalized root mean square error (NRMSE) varying, from 8 to 20%, and a normalized mean bias error (NMBE) ranging from –9 to 26%, for those variables. Based on these results, and in order to improve the accuracy of the NASA POWER dataset, two bias corrections were performed to all weather variables: one for the Alentejo Region as a whole; another, for each location individually. Results improved significantly, especially when a local bias correction is performed, with Tmax and Tmin presenting an improvement of the mean NRMSE of 6.6 °C (from 8.0 °C) and 16.1 °C (from 20.5 °C), respectively, while a mean NMBE decreased from 10.65 to 0.2%. Rs results also show a very high goodness of fit with a mean NRMSE of 11.2% and mean NMBE equal to 0.1%. Additionally, bias corrected RH data performed acceptably with an NRMSE lower than 12.1% and an NMBE below 2.1%. However, even when a bias correction is performed, Ws lacks the performance showed by the remaining weather variables, with an NRMSE never lower than 19.6%. Results show that NASA POWER can be useful for the generation of weather data sets where ground weather stations data is of missing or unavailable.

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Computational Modeling of Music Cognition: A Case Study on Model Selection

Music Perception An Interdisciplinary Journal ◽

10.1525/mp.2006.23.5.365 ◽

2006 ◽

Vol 23 (5) ◽

pp. 365-376 ◽

Cited By ~ 21

Author(s):

Henkjan Honing

Keyword(s):

Model Selection ◽

Strong Evidence ◽

Goodness Of Fit ◽

Computational Models ◽

Recent Literature ◽

Music Cognition ◽

Kinematic Model ◽

Music Performance ◽

Limited Range

While the most common way of evaluating a computational model is to see whether it shows a good fit with the empirical data, recent literature on theory testing and model selection criticizes the assumption that this is actually strong evidence for the validity of a model. This article presents a case study from music cognition (modeling the ritardandi in music performance) and compares two families of computational models (kinematic and perceptual) using three different model selection criteria: goodness-of-fit, model simplicity, and the degree of surprise in the predictions. In the light of what counts as strong evidence for a model’s validity—namely that it makes limited range, nonsmooth, and relatively surprising predictions—the perception-based model is preferred over the kinematic model.

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BIDIRECTIONAL REFLECTANCE MODELING OF THE GEOSTATIONARY SENSOR HIMAWARI-8/AHI USING A KERNEL-DRIVEN BRDF MODEL

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsannals-iii-7-3-2016 ◽

2016 ◽

Vol III-7 ◽

pp. 3-8 ◽

Cited By ~ 2

Author(s):

M. Matsuoka ◽

M. Takagi ◽

S. Akatsuka ◽

R. Honda ◽

A. Nonomura ◽

...

Keyword(s):

Surface Properties ◽

Land Surface ◽

Goodness Of Fit ◽

Near Infrared ◽

Temporal Frequency ◽

Atmospheric Correction ◽

Time Of Day ◽

Coefficient Of Determination ◽

Near Infrared Reflectance ◽

Bidirectional Reflectance

Himawari-8/AHI is a new geostationary sensor that can observe the land surface with high temporal frequency. Bidirectional reflectance derived by the Advanced Himawari Imager (AHI) includes information regarding land surface properties such as albedo, vegetation condition, and forest structure. This information can be extracted by modeling bidirectional reflectance using a bidirectional reflectance distribution function (BRDF). In this study, a kernel-driven BRDF model was applied to the red and near infrared reflectance observed over 8 hours during daytime to express intraday changes in reflectance. We compared the goodness of fit for six combinations of model kernels. The Ross-Thin and Ross-Thick kernels were selected as the best volume kernels for the red and near infrared bands, respectively. For the geometric kernel, the Li-sparse-Reciprocal and Li-Dense kernels displayed similar goodness of fit. The coefficient of determination and regression residuals showed a strong dependency on the azimuth angle of land surface slopes and the time of day that observations were made. Atmospheric correction and model adjustment of the terrain were the main issues encountered. These results will help to improve the BRDF model and to extract surface properties from bidirectional reflectance.

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Mathematical Models for Tumor Growth and the Reduction of Overtreatment

Journal of Neurological Surgery Part B Skull Base ◽

10.1055/s-0038-1667148 ◽

2018 ◽

Vol 80 (01) ◽

pp. 072-078 ◽

Cited By ~ 2

Author(s):

Berdine Heesterman ◽

John-Melle Bokhorst ◽

Lisa de Pont ◽

Berit Verbist ◽

Jean-Pierre Bayley ◽

...

Keyword(s):

Tumor Growth ◽

Mathematical Models ◽

Goodness Of Fit ◽

Mean Squared Error ◽

Age At Onset ◽

Growth Curves ◽

Magnetic Resonance Images ◽

Coefficient Of Determination ◽

Growth Data ◽

Significant Difference

Background To improve our understanding of the natural course of head and neck paragangliomas (HNPGL) and ultimately differentiate between cases that benefit from early treatment and those that are best left untreated, we studied the growth dynamics of 77 HNPGL managed with primary observation. Methods Using digitally available magnetic resonance images, tumor volume was estimated at three time points. Subsequently, nonlinear least squares regression was used to fit seven mathematical models to the observed growth data. Goodness of fit was assessed with the coefficient of determination (R 2) and root-mean-squared error. The models were compared with Kruskal–Wallis one-way analysis of variance and subsequent post-hoc tests. In addition, the credibility of predictions (age at onset of neoplastic growth and estimated volume at age 90) was evaluated. Results Equations generating sigmoidal-shaped growth curves (Gompertz, logistic, Spratt and Bertalanffy) provided a good fit (median R 2: 0.996–1.00) and better described the observed data compared with the linear, exponential, and Mendelsohn equations (p < 0.001). Although there was no statistically significant difference between the sigmoidal-shaped growth curves regarding the goodness of fit, a realistic age at onset and estimated volume at age 90 were most often predicted by the Bertalanffy model. Conclusions Growth of HNPGL is best described by decelerating tumor growth laws, with a preference for the Bertalanffy model. To the best of our knowledge, this is the first time that this often-neglected model has been successfully fitted to clinically obtained growth data.

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Abdominal subcutaneous fat quantification in obese patients from limited field-of-view MRI data

Scientific Reports ◽

10.1038/s41598-020-75985-8 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Sophia Michel ◽

Nicolas Linder ◽

Tobias Eggebrecht ◽

Alexander Schaudinn ◽

Matthias Blüher ◽

...

Keyword(s):

Gender Differences ◽

Adipose Tissue ◽

Goodness Of Fit ◽

Imaging Techniques ◽

Subcutaneous Fat ◽

Field Of View ◽

Coefficient Of Determination ◽

Obese Patients ◽

Obese Subjects ◽

Surrogate Measures

Abstract Different types of adipose tissue can be accurately localized and quantified by tomographic imaging techniques (MRI or CT). One common shortcoming for the abdominal subcutaneous adipose tissue (ASAT) of obese subjects is the technically restricted imaging field of view (FOV). This work derives equations for the conversion between six surrogate measures and fully segmented ASAT volume and discusses the predictive power of these image-based quantities. Clinical (gender, age, anthropometry) and MRI data (1.5 T, two-point Dixon sequence) of 193 overweight and obese patients (116 female, 77 male) from a single research center for obesity were analyzed retrospectively. Six surrogate measures of fully segmented ASAT volume (VASAT) were considered: two simple ASAT lengths, two partial areas (Ap-FH, Ap-ASIS) and two partial volumes (Vp-FH, Vp-ASIS) limited by either the femoral heads (FH) or the anterior superior iliac spine (ASIS). Least-squares regression between each measure and VASAT provided slope and intercept for the computation of estimated ASAT volumes (V~ASAT). Goodness of fit was evaluated by coefficient of determination (R2) and standard deviation of percent differences (sd%) between V~ASAT and VASAT. Best agreement was observed for partial volume Vp-FH (sd% = 14.4% and R2 = 0.78), followed by Vp-ASIS (sd% = 18.1% and R2 = 0.69) and AWFASIS (sd% = 23.9% and R2 = 0.54), with minor gender differences only. Other estimates from simple lengths and partial areas were moderate only (sd% > 23.0% and R2 < 0.50). Gender differences in R2 generally ranged between 0.02 (dven) and 0.29 (Ap-FH). The common FOV restriction for MRI volumetry of ASAT in obese subjects can best be overcome by estimating VASAT from Vp-FH using the equation derived here. The very simple AWFASIS can be used with reservation.

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Relationship between positive clinical VTO and post-treatment soft tissue profile following phase l growth modification therapy

Orthodontic Journal of Nepal ◽

10.3126/ojn.v8i2.23071 ◽

2018 ◽

Vol 8 (2) ◽

pp. 45-49 ◽

Cited By ~ 1

Author(s):

Shabbir Hussain ◽

Muhammad Azeem ◽

Waheed Ul Hamid ◽

Faiz Rasool

Keyword(s):

Soft Tissue ◽

Goodness Of Fit ◽

Linear Regression Analysis ◽

Post Treatment ◽

Coefficient Of Determination ◽

Facial Profile ◽

Soft Tissue Profile ◽

Growth Modification ◽

Treatment Profile

Introduction: Facial profile improvement is goal of cotemporary orthodontics and a reason to seek orthodontic therapy. The soft tissue profile plays a important role on orthodontic diagnosis and treatment planning. The objective of this study is to investigate the relationship between positive clinical VTO and actual post-treatment soft tissue profile after phase l therapy of growth modification in Class II. Materials & Method: Pretreatment simulation of post-treatment and actual post-treatment profile photographs of 30 class ll div l patients treated with twin block appliance were compared. Three profile photographs of each subject; pretreatment, positive clinical VTO and post-treatment were taken and on each photograph four angles; Nasofacial (NF), Nasomental (NM), Mentocervical (MC) and Nasolabial (NL) were drawn and measured. Mean, standard deviation, success and coefficient of determination of each angle was measured and linear regressions analysis was applied to find out the correlation. Result: Nasolabial and nasomental angles showed greater success i.e. 81.4% and 68.1% respectively showing greater correlation, while nasofacial and mentocervical angles showed less success i.e. 48.1% and 48.3% respectively showing less correlation. Linear regression analysis revealed that positive clinical VTO significantly predicted post-treatment profile whereas coefficient of determination for nasomental and mentocervical angles was 76.5% and 60% representing a better goodness of fit while nasolabial and nasofacial angles was 53.6% and 51.6% demonstrating poor fit of regression lines. Conclusion: Even though there is improved facial profile obtained by protracting the mandible into class l relation in a chair side maneuver in class ll div l malocclusions, yet the orthodontist should be tentative when predicting the outcome of growth modification to get benefit of this therapy.

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Computational Modeling of Skull Bone Structures and Simulation of Skull Fractures Using the YEAHM Head Model

Biology ◽

10.3390/biology9090267 ◽

2020 ◽

Vol 9 (9) ◽

pp. 267 ◽

Cited By ~ 1

Author(s):

Alcino Barbosa ◽

Fábio A. O. Fernandes ◽

Ricardo J. Alves de Sousa ◽

Mariusz Ptak ◽

Johannes Wilhelm

Keyword(s):

Computational Models ◽

Soft Tissues ◽

Constitutive Models ◽

Human Head ◽

Head Model ◽

Head Impacts ◽

Skull Model ◽

Linear Behavior ◽

Local Material ◽

Direct Head

The human head is a complex multi-layered structure of hard and soft tissues, governed by complex materials laws and interactions. Computational models of the human head have been developed over the years, reaching high levels of detail, complexity, and precision. However, most of the attention has been devoted to the brain and other intracranial structures. The skull, despite playing a major role in direct head impacts, is often overlooked and simplified. In this work, a new skull model is developed for the authors’ head model, the YEAHM, based on the original outer geometry, but segmenting it with sutures, diploë, and cortical bone, having variable thickness across different head sections and based on medical craniometric data. These structures are modeled with constitutive models that consider the non-linear behavior of skull bones and also the nature of their failure. Several validations are performed, comparing the simulation results with experimental results available in the literature at several levels: (i) local material validation; (ii) blunt trauma from direct impact against stationary skull; (iii) three impacts at different velocities simulating falls; (iv) blunt ballistic temporoparietal head impacts. Accelerations, impact forces, and fracture patterns are used to validate the skull model.

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