scholarly journals Predictive Models with Patient Specific Material Properties for the Biomechanical Behavior of Ascending Thoracic Aneurysms

2015 ◽  
Vol 44 (1) ◽  
pp. 84-98 ◽  
Author(s):  
Olfa Trabelsi ◽  
Ambroise Duprey ◽  
Jean-Pierre Favre ◽  
Stéphane Avril
Keyword(s):  
Predictive Models ◽  
Material Properties ◽  
Patient Specific ◽  
Biomechanical Behavior ◽  
Specific Material ◽  
Thoracic Aneurysms

scholarly journals Can we obtain in vivo transmural mean hoop stress of the aortic wall without knowing patient-specific material properties and residual deformations?

2018 ◽  
Author(s):  
Minliang Liu ◽  
Liang Liang ◽  
Haofei Liu ◽  
Ming Zhang ◽  
Caitlin Martin ◽  
...  
Keyword(s):  
Material Properties ◽  
Hoop Stress ◽  
Aortic Wall ◽  
Patient Specific ◽  
Membrane Stress ◽  
Penalty Approach ◽  
Thin Walled ◽  
Specific Material ◽  
Residual Deformations

AbstractIt is well known that residual deformations/stresses alter the mechanical behavior of arteries, e.g. the pressure-diameter curves. In an effort to enable personalized analysis of the aortic wall stress, approaches have been developed to incorporate experimentally-derived residual deformations into in vivo loaded geometries in finite element simulations using thick-walled models. Solid elements are typically used to account for “bending-like” residual deformations. Yet, the difficulty in obtaining patient-specific residual deformations and material properties has become one of the biggest challenges of these thick-walled models. In thin-walled models, fortunately, static determinacy offers an appealing prospect that allows for the calculation of the thin-walled membrane stress without patient-specific material properties. The membrane stress can be computed using forward analysis by enforcing an extremely stiff material property as penalty treatment, which is referred to as the forward penalty approach. However, thin-walled membrane elements, which have zero bending stiffness, are incompatible with the residual deformations, and therefore, it is often stated as a limitation of thin-walled models. In this paper, by comparing the predicted stresses from thin-walled models and thick-walled models, we demonstrate that the transmural mean hoop stress is the same for the two models and can be readily obtained from in vivo clinical images without knowing the patient-specific material properties and residual deformations. Computation of patient-specific mean hoop stress can be greatly simplified by using membrane model and the forward penalty approach, which may be clinically valuable.

scholarly journals 3D Plotting of Silica/Collagen Xerogel Granules in an Alginate Matrix for Tissue-Engineered Bone Implants

Materials ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 830
Author(s):  
Sina Rößler ◽  
Andreas Brückner ◽  
Iris Kruppke ◽  
Hans-Peter Wiesmann ◽  
Thomas Hanke ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Bone Regeneration ◽  
Material Properties ◽  
Viscoelastic Properties ◽  
Viscoelastic Behavior ◽  
Matrix Phase ◽  
Patient Specific ◽  
Active Player ◽  
Alginate Concentration ◽  
Alginate Matrix

Today, materials designed for bone regeneration are requested to be degradable and resorbable, bioactive, porous, and osteoconductive, as well as to be an active player in the bone-remodeling process. Multiphasic silica/collagen Xerogels were shown, earlier, to meet these requirements. The aim of the present study was to use these excellent material properties of silica/collagen Xerogels and to process them by additive manufacturing, in this case 3D plotting, to generate implants matching patient specific shapes of fractures or lesions. The concept is to have Xerogel granules as active major components embedded, to a large proportion, in a matrix that binds the granules in the scaffold. By using viscoelastic alginate as matrix, pastes of Xerogel granules were processed via 3D plotting. Moreover, alginate concentration was shown to be the key to a high content of irregularly shaped Xerogel granules embedded in a minimum of matrix phase. Both the alginate matrix and Xerogel granules were also shown to influence viscoelastic behavior of the paste, as well as the dimensionally stability of the scaffolds. In conclusion, 3D plotting of Xerogel granules was successfully established by using viscoelastic properties of alginate as matrix phase.

Abstract 562: Human Carotid Atherosclerosis Plaque Progression and Vessel Material Stiffness at Follow Up Had Positive Correlation: An in vivo Vessel Stiffness MRI Follow Up Study

2017 ◽  
Vol 37 (suppl_1) ◽  
Author(s):  
Qingyu Wang ◽  
Dalin Tang ◽  
Gador Canton ◽  
Jian Guo ◽  
Xiaoya Guo ◽  
...  
Keyword(s):  
Negative Correlation ◽  
Material Properties ◽  
Stretch Ratio ◽  
Patient Specific ◽  
Plaque Progression ◽  
Material Stiffness ◽  
Variation Rate ◽  
Vessel Material

It is hypothesized that artery stiffness may be associated with plaque progression. However, in vivo vessel material stiffness follow-up data is lacking in the literature. In vivo 3D multi-contrast and Cine magnetic resonance imaging (MRI) carotid plaque data were acquired from 8 patients with follow-up (18 months) with written informed consent obtained. Cine MRI and 3D thin-layer models were used to determine parameter values of the Mooney-Rivlin models for the 81slices from 16 plaques (2 scans/patient) using our established iterative procedures. Effective Young’s Modulus (YM) values for stretch ratio [1.0,1.3] were calculated for each slice for analysis. Stress-stretch ratio curves from Mooney-Rivlin models for the 16 plaques and 81 slices are given in Fig. 1. Average YM value of the 81 slices was 411kPa. Slice YM values varied from 70 kPa (softest) to 1284 kPa (stiffest), a 1734% difference. Average slice YM values by vessel varied from 109 kPa (softest) to 922 kPa (stiffest), a 746% difference. Location-wise, the maximum slice YM variation rate within a vessel was 306% (139 kPa vs. 564 kPa). Average slice YM variation rate within a vessel for the 16 vessels was 134%. Average variation of YM values from baseline (T1) to follow up (T2) for all patients was 61.0%. The range of the variation of YM values was [-28.4%, 215%]. For progression study, YM increase (YMI=YM T2 -TM T1 ) showed negative correlation with plaque progression measured by wall thickness increase (WTI), (r= -0.6802, p=0.0634). YM T2 showed strong negative correlation with WTI (r= -0.7764, p=0.0235). Correlation between YM T1 and WTI was not significant (r= -0.4353, p= 0.2811). Conclusion In vivo carotid vessel material properties have large variations from patient to patient, along the vessel segment within a patient, and from baseline to follow up. Use of patient-specific, location specific and time-specific material properties could potentially improve the accuracy of model stress/strain calculations.

Effects of infill patterns on the strength and stiffness of 3D printed topologically optimized geometries

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nadim S. Hmeidat ◽  
Bailey Brown ◽  
Xiu Jia ◽  
Natasha Vermaak ◽  
Brett Compton
Keyword(s):  
Material Properties ◽  
Failure Modes ◽  
Mechanical Anisotropy ◽  
Failure Behavior ◽  
Fused Filament Fabrication ◽  
Content Type ◽  
Orthotropic Composite ◽  
Material Extrusion ◽  
Specific Material ◽  
Strength And Stiffness

Purpose Mechanical anisotropy associated with material extrusion additive manufacturing (AM) complicates the design of complex structures. This study aims to focus on investigating the effects of design choices offered by material extrusion AM – namely, the choice of infill pattern – on the structural performance and optimality of a given optimized topology. Elucidation of these effects provides evidence that using design tools that incorporate anisotropic behavior is necessary for designing truly optimal structures for manufacturing via AM. Design/methodology/approach A benchmark topology optimization (TO) problem was solved for compliance minimization of a thick beam in three-point bending and the resulting geometry was printed using fused filament fabrication. The optimized geometry was printed using a variety of infill patterns and the strength, stiffness and failure behavior were analyzed and compared. The bending tests were accompanied by corresponding elastic finite element analyzes (FEA) in ABAQUS. The FEA used the material properties obtained during tensile and shear testing to define orthotropic composite plies and simulate individual printed layers in the physical specimens. Findings Experiments showed that stiffness varied by as much as 22% and failure load varied by as much as 426% between structures printed with different infill patterns. The observed failure modes were also highly dependent on infill patterns with failure propagating along with printed interfaces for all infill patterns that were consistent between layers. Elastic FEA using orthotropic composite plies was found to accurately predict the stiffness of printed structures, but a simple maximum stress failure criterion was not sufficient to predict strength. Despite this, FE stress contours proved beneficial in identifying the locations of failure in printed structures. Originality/value This study quantifies the effects of infill patterns in printed structures using a classic TO geometry. The results presented to establish a benchmark that can be used to guide the development of emerging manufacturing-oriented TO protocols that incorporate directionally-dependent, process-specific material properties.

scholarly journals Covid19Risk.ai: An open source repository and online calculator of prediction models for early diagnosis and prognosis of Covid-19

2021 ◽  
Author(s):  
Iva Halilaj ◽  
Avishek Chatterjee ◽  
Yvonka van Wijk ◽  
Guangyao Wu ◽  
Brice van Eeckhout ◽  
...  
Keyword(s):  
Open Source ◽  
Predictive Models ◽  
Prediction Models ◽  
Patient Specific ◽  
Diagnosis And Prognosis ◽  
Online Calculator ◽  
User Friendly ◽  
Type 3 ◽  
Proof Of Principle

AbstractObjectiveThe current pandemic has led to a proliferation of predictive models being developed to address various aspects of COVID-19 patient care. We aimed to develop an online platform that would serve as an open source repository for a curated subset of such models, and provide a simple interface for included models to allow for online calculation. This platform would support doctors during decision-making regarding diagnoses, prognoses, and follow-up of COVID-19 patients, expediting the models’ transition from research to clinical practice.MethodsIn this proof-of-principle study, we performed a literature search in PubMed and WHO database to find suitable models for implementation on our platform. All selected models were publicly available (peer reviewed publications or open source repository) and had been validated (TRIPOD type 3 or 2b). We created a method for obtaining the regression coefficients if only the nomogram was available in the original publication. All predictive models were transcribed on a practical graphical user interface using PHP 8.0.0, and published online together with supporting documentation and links to the associated articles.ResultsThe open source website https://covid19risk.ai/ currently incorporates nine models from six different research groups, evaluated on datasets from different countries. The website will continue to be populated with other models related to COVID-19 prediction as these become available. This dynamic platform allows COVID-19 researchers to contact us to have their model curated and included on our website, thereby increasing the reach and real-world impact of their work.ConclusionWe have successfully demonstrated in this proof-of-principle study that our website provides an inclusive platform for predictive models related to COVID-19. It enables doctors to supplement their judgment with patient-specific predictions from externally-validated models in a user-friendly format. Additionally, this platform supports researchers in showcasing their work, which will increase the visibility and use of their models.

Multi-level patient-specific modelling of the proximal femur. A promising tool to quantify the effect of osteoporosis treatment

2009 ◽  
Vol 367 (1895) ◽  
pp. 2079-2093 ◽  
Author(s):  
Leen Lenaerts ◽  
G. Harry van Lenthe
Keyword(s):  
Bone Strength ◽  
Material Properties ◽  
Bone Structure ◽  
Structural Information ◽  
Response To Treatment ◽  
Patient Specific ◽  
Loading Conditions ◽  
Femoral Bone ◽  
Fe Modelling ◽  
Specific Loading

Preventing femoral fractures is an important goal in osteoporosis research. In order to evaluate a person's fracture risk and to quantify response to treatment, bone competence is best assessed by bone strength. Finite-element (FE) modelling based on medical imaging is considered a very promising technique for the assessment of in vivo femoral bone strength. Over the past decades, a number of different FE models have been presented focusing on the effect of several methodological aspects, such as mesh type, material properties and loading conditions, on the precision and accuracy of these models. In this paper, a review of this work is presented. We conclude that moderate to good predictions can be made, especially when the models are tuned to specific loading scenarios. However, there is room for improvement when multiple loading conditions need to be evaluated. We hypothesize that including anisotropic material properties is the first target. As a proof of the concept, we demonstrate that the main orientation of the femoral bone structure can be calculated from clinical computed tomography scans. We hypothesize that this structural information can be used to estimate the anisotropic bone material properties, and that in the future this could potentially lead to a greater predictive value of FE models for femoral bone strength.

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