Preliminary Murine Aortic Tissue Material Properties From Pressure-Diameter Experiments

2007 ◽  
Author(s):  
Mark E. Rentschler ◽  
B. Timothy Baxter
Keyword(s):  
Material Properties ◽  
Patient Specific ◽  
Aortic Tissue ◽  
Specific Information ◽  
Transverse Diameter ◽  
Impending Rupture ◽  
Abdominal Aortic ◽  
History Of ◽  
Tissue Material ◽  
Progressive Enlargement

Abdominal aortic aneurysm (AAA) is a common and deadly problem. The aortic diameter increases in association with a complex remodeling process that includes changes in the structure and content of key proteins, elastin and collagen. As these changes occur, the tissue mechanical properties also change. The natural history of AAA is progressive enlargement to a point of mechanical tissue failure typically followed by death. Currently, the marker used to predict the risk of impending rupture is the largest transverse diameter. After reaching a diameter threshold of 5.5 cm the aneurysm is surgically repaired. This criterion does not consider any patient-specific information or the known heterogeneity of the aneurysm that may, in some cases, lead to rupture before the aneurysm reaches the standard intervention threshold. Conversely, in many patients, continued observation beyond this threshold is safe.

Analysis and Modeling of Aortic Tissue Material Properties

2004 ◽  
Author(s):  
Kurosh Darvish ◽  
Libor Lobovsky ◽  
Sang-Hyun Lee
Keyword(s):  
Experimental Data ◽  
Mechanical Behavior ◽  
Material Properties ◽  
Linear Viscoelasticity ◽  
Hyperelastic Material ◽  
Aortic Tissue ◽  
New Material ◽  
Tissue Material

A hyperelastic material with linear viscoelasticity was used to characterize the mechanical behavior of aortic tissue based on literature and new experimental data. It was shown that the previous data led to contradictory uniaxial and biaxial responses. A set of new material properties were identified which closely described the experimental data for strains below 40%.

Effects of Wall Calcifications in Patient-Specific Wall Stress Analyses of Abdominal Aortic Aneurysms

2006 ◽  
Vol 129 (1) ◽  
pp. 105-109 ◽  
Author(s):  
Lambert Speelman ◽  
Ajay Bohra ◽  
E. Marielle H. Bosboom ◽  
Geert Willem H. Schurink ◽  
Frans N. van de Vosse ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Finite Element ◽  
Material Properties ◽  
Peak Stress ◽  
Wall Stress ◽  
Ct Scans ◽  
Patient Specific ◽  
Stress Distributions ◽  
Rupture Risk ◽  
Abdominal Aortic

It is generally acknowledged that rupture of an abdominal aortic aneurysm (AAA) occurs when the stress acting on the wall over the cardiac cycle exceeds the strength of the wall. Peak wall stress computations appear to give a more accurate rupture risk assessment than AAA diameter, which is currently used for a diagnose. Despite the numerous studies utilizing patient-specific wall stress modeling of AAAs, none investigated the effect of wall calcifications on wall stress. The objective of this study was to evaluate the influence of calcifications on patient-specific finite element stress computations. In addition, we assessed whether the effect of calcifications could be predicted directly from the CT-scans by relating the effect to the amount of calcification present in the AAA wall. For 6 AAAs, the location and extent of calcification was identified from CT-scans. A finite element model was created for each AAA and the areas of calcification were defined node-wise in the mesh of the model. Comparisons are made between maximum principal stress distributions, computed without calcifications and with calcifications with varying material properties. Peak stresses are determined from the stress results and related to a calcification index (CI), a quantification of the amount of calcification in the AAA wall. At calcification sites, local stresses increased, leading to a peak stress increase of 22% in the most severe case. Our results displayed a weak correlation between the CI and the increase in peak stress. Additionally, the results showed a marked influence of the calcification elastic modulus on computed stresses. Inclusion of calcifications in finite element analysis of AAAs resulted in a marked alteration of the stress distributions and should therefore be included in rupture risk assessment. The results also suggest that the location and shape of the calcified regions—not only the relative amount—are considerations that influence the effect on AAA wall stress. The dependency of the effect of the wall stress on the calcification elastic modulus points out the importance of determination of the material properties of calcified AAA wall.

scholarly journals Parametric study of effects of collagen turnover on the natural history of abdominal aortic aneurysms

2013 ◽  
Vol 469 (2150) ◽  
pp. 20120556 ◽  
Author(s):  
J. S. Wilson ◽  
S. Baek ◽  
J. D. Humphrey
Keyword(s):  
Natural History ◽  
Collagen Fibre ◽  
Abdominal Aortic Aneurysms ◽  
Aortic Aneurysms ◽  
Patient Specific ◽  
Collagen Turnover ◽  
Turnover Rates ◽  
Clinical Endpoints ◽  
Abdominal Aortic ◽  
History Of

Abdominal aortic aneurysms (AAAs) are characterized by significant changes in the architecture of the aortic wall, notably, loss of functional elastin and smooth muscle. Because collagen is the principal remaining load-bearing constituent of the aneurysmal wall, its turnover must play a fundamental role in the natural history of the lesion. Nevertheless, detailed investigations of the effects of different aspects of collagen turnover on AAA development are lacking. A finite-element membrane model of the growth and remodelling of idealized AAAs was thus used to investigate parametrically four of the primary aspects of collagen turnover: rates of production, half-life, deposition stretch (prestretch) and material stiffness. The predicted rates of aneurysmal expansion and spatio-temporal changes in wall thickness, biaxial stresses and maximum collagen fibre stretch at the apex of the lesion depended strongly on all four factors, as did the predicted clinical endpoints (i.e. arrest, progressive expansion or rupture). Collagen turnover also affected the axial expansion, largely due to mechanical changes within the shoulder region of the lesion. We submit, therefore, that assessment of rupture risk could be improved by future experiments that delineate and quantify different aspects of patient-specific collagen turnover and that such understanding could lead to new targeted therapeutics.

Abstract 105: Association of Viscoelastic Material Properties and Extracellular Matrix Remodeling in Human Abdominal Aortic Aneurysmal Tissue

2017 ◽  
Vol 37 (suppl_1) ◽  
Author(s):  
Doran S Mix ◽  
Sandra A Toth ◽  
Ibrahima Bah ◽  
Michael C Stoner ◽  
Bruce I Goldman ◽  
...  
Keyword(s):  
Extracellular Matrix ◽  
Western Blot ◽  
Viscoelastic Material ◽  
Material Properties ◽  
Aortic Wall ◽  
Cyclic Strain ◽  
Wall Stress ◽  
Aortic Tissue ◽  
Matrix Remodeling ◽  
Abdominal Aortic

Objectives: Predicting rupture of abdominal aortic aneurysm (AAA) requires knowledge of both the rate of extracellular matrix (ECM) degradation and the pulsatile stress on the aortic tissue. The activity of matrix metallopeptidase 9 (MMP9) and its inhibitor, TIMP1, are associated with alterations in aortic ECM but it is unknown if these changes effect the dynamic viscoelastic properties. We hypothesize that increased levels of MMP9 within AAA tissue will be associated with a greater dynamic modulus (E*), as a surrogate of increased aortic wall stress. Methods: Human aneurysmal aortic tissue was obtained at the time of open AAA repair (n=11) and age-matched non-aneurysmal cadavers (n=10). Uniaxial viscoelastic material properties were measured in the circumferential orientation under physiologic preload (110 mmHg) and cyclic strain (± 5%@1Hz). Quantitative histologic and immunohistochemistry were preformed using Fiji imaging software. Aortic MMP9 and TIMP1 content and activity were quantified using western blot and zymography. Results: E* was greater (1862±464 vs 1362±405 kPa, p=0.02) in the AAA tissue as compared to non-aneurysmal tissue. AAA tissue contained less elastin (6.7±6.7 vs 23.4±8.7%, p=0.01) and a greater collagen/elastin ratio (19.9±20.6 vs 2.3±2.5%, p=0.05). Immunohistochemistry revealed 200% greater MMP9 content in the AAA tissue (Figure A & B, 0.61 vs 0.03%, p=0.03). Increased MMP9 content was confirmed using a western blot (0.43 vs 0.06 AU, p<0.01). No difference in relative MMP9 activity (4307 vs 2324 AU, p=0.25) or level of TIMP1 (0.03 vs 0.02, p=0.6) were observed. There was a positive linear correlation (Figure C, r 2 =0.47) between E* and MMP9 as determined by quantitative immunohistochemistry. Conclusions: Our data suggests a positive relationship between E* and MMP9 content. Increased tissue stiffness may trigger MMP9 production resulting in a positive-feedback loop, progressively increasing aortic wall stress and rupture risk.

scholarly journals A Geodesics-Based Surface Parameterization to Assess Aneurysm Progression

2016 ◽  
Vol 138 (5) ◽  
Author(s):  
Ly Phan ◽  
Katherine Courchaine ◽  
Amir Azarbal ◽  
David Vorp ◽  
Cindy Grimm ◽  
...  
Keyword(s):  
Shape Change ◽  
Strain Relaxation ◽  
Patient Specific ◽  
Transverse Diameter ◽  
Missing Information ◽  
Registration Method ◽  
Time Points ◽  
Surface Parameterization ◽  
Change Dynamics ◽  
Abdominal Aortic

Abdominal aortic aneurysm (AAA) intervention and surveillance is currently based on maximum transverse diameter, even though it is recognized that this might not be the best strategy. About 10% of patients with small AAA transverse diameters, for whom intervention is not considered, still rupture; while patients with large AAA transverse diameters, for whom intervention would have been recommended, have stable aneurysms that do not rupture. While maximum transverse diameter is easy to measure and track in clinical practice, one of its main drawbacks is that it does not represent the whole AAA and rupture seldom occurs in the region of maximum transverse diameter. By following maximum transverse diameter alone clinicians are missing information on the shape change dynamics of the AAA, and clues that could lead to better patient care. We propose here a method to register AAA surfaces that were obtained from the same patient at different time points. Our registration method could be used to track the local changes of the patient-specific AAA. To achieve registration, our procedure uses a consistent parameterization of the AAA surfaces followed by strain relaxation. The main assumption of our procedure is that growth of the AAA occurs in such a way that surface strains are smoothly distributed, while regions of small and large surface growth can be differentiated. The proposed methodology has the potential to unravel different patterns of AAA growth that could be used to stratify patient risks.

Use of Regional Mechanical Properties of Abdominal Aortic Aneurysms to Advance Finite Element Modeling of Rupture Risk

2012 ◽  
Author(s):  
Áine P. Tierney ◽  
Anthony Callanan ◽  
Timothy M. McGloughlin
Keyword(s):  
Mechanical Properties ◽  
Stress Distribution ◽  
Aortic Aneurysms ◽  
Patient Specific ◽  
Aortic Tissue ◽  
Structural Problems ◽  
Aneurysm Wall ◽  
Abdominal Aortic ◽  
Computer Based ◽  
Complex Structural

AAA rupture represents a catastrophic failure of the degenerated aortic tissue when the aneurysm wall can no longer withstand the stresses on it. Understanding the stress distribution in the aneurysm, along with its material properties, is an essential step toward predicting the rupture of an AAA. FE analysis is a computer-based method of solving complex structural problems for which the stress distribution can be easily studied. In analyzing AAA behavior with FE models, realistic aortic anatomies with patient-specific physiological and mechanical properties have been used in recent years. Data for these anatomies have typically come from computed tomography (CT) scans.

Tailoring Communications for Primary Care Settings

1998 ◽  
Vol 37 (02) ◽  
pp. 171-178 ◽  
Author(s):  
B. Glassman ◽  
B. K. Rimer
Keyword(s):  
Primary Care ◽  
Health Professionals ◽  
Contact Time ◽  
Behavioral Changes ◽  
Patient Specific ◽  
Specific Information ◽  
Medical Settings ◽  
Primary Care Settings ◽  
Professional Contact ◽  
Tailored Communications

AbstractIn more and more medical settings, physicians have less and less time to be effective communicators. To be effective, they need accurate, current information about their patients. Tailored health communications can facilitate positive patient-provider communications and foster behavioral changes conducive to health. Tailored communications (TCs) are produced for an individual based on information about that person. The focus of this report is on tailored print communications (TPCs). TPCs also enhance the process of evaluation, because they require a database and the collection of patient-specific information. We present a Tailoring Model for Primary Care that describes the steps involved in creating TPCs. We also provide examples from three ongoing studies in which TPCs are being used in order to illustrate the kinds of variables used for tailoring the products that are developed and how evaluation is conducted. TPCs offer opportunities to expand the reach of health professionals and to give personalized, individualized massages in an era of shrinking professional contact time.

scholarly journals Assessment of patient specific information in the wild on fundus photography and optical coherence tomography

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marion R. Munk ◽  
Thomas Kurmann ◽  
Pablo Márquez-Neila ◽  
Martin S. Zinkernagel ◽  
Sebastian Wolf ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cross Sections ◽  
Underlying Disease ◽  
Geographic Atrophy ◽  
Clinical Population ◽  
Fundus Photography ◽  
Peak Performance ◽  
Patient Specific ◽  
Specific Information ◽  
Fundus Images

AbstractIn this paper we analyse the performance of machine learning methods in predicting patient information such as age or sex solely from retinal imaging modalities in a heterogeneous clinical population. Our dataset consists of N = 135,667 fundus images and N = 85,536 volumetric OCT scans. Deep learning models were trained to predict the patient’s age and sex from fundus images, OCT cross sections and OCT volumes. For sex prediction, a ROC AUC of 0.80 was achieved for fundus images, 0.84 for OCT cross sections and 0.90 for OCT volumes. Age prediction mean absolute errors of 6.328 years for fundus, 5.625 years for OCT cross sections and 4.541 for OCT volumes were observed. We assess the performance of OCT scans containing different biomarkers and note a peak performance of AUC = 0.88 for OCT cross sections and 0.95 for volumes when there is no pathology on scans. Performance drops in case of drusen, fibrovascular pigment epitheliuum detachment and geographic atrophy present. We conclude that deep learning based methods are capable of classifying the patient’s sex and age from color fundus photography and OCT for a broad spectrum of patients irrespective of underlying disease or image quality. Non-random sex prediction using fundus images seems only possible if the eye fovea and optic disc are visible.

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