Stress-Relaxation and Cyclic Behavior of Human Carotid Plaque Tissue

Applied use of biomechanical measurements from human tissues for the development of medical skills trainers: a scoping review

OBJECTIVE

The objective of this review was to identify quantitative biomechanical measurements of human tissues, the methods for obtaining these measurements, and the primary motivations for conducting biomechanical research.

INTRODUCTION

Medical skills trainers are a safe and useful tool for clinicians to use when learning or practicing medical procedures. The haptic fidelity of these devices is often poor, which may be because the synthetic materials chosen for these devices do not have the same mechanical properties as human tissues. This review investigates a heterogeneous body of literature to identify which biomechanical properties are available for human tissues, the methods for obtaining these values, and the primary motivations behind conducting biomechanical tests.

INCLUSION CRITERIA

Studies containing quantitative measurements of the biomechanical properties of human tissues were included. Studies that primarily focused on dynamic and fluid mechanical properties were excluded. Additionally, studies only containing animal, in silico , or synthetic materials were excluded from this review.

METHODS

This scoping review followed the JBI methodology for scoping reviews and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). Sources of evidence were extracted from CINAHL (EBSCO), IEEE Xplore, MEDLINE (PubMed), Scopus, and engineering conference proceedings. The search was limited to the English language. Two independent reviewers screened titles and abstracts as well as full-text reviews. Any conflicts that arose during screening and full-text review were mediated by a third reviewer. Data extraction was conducted by 2 independent reviewers and discrepancies were mediated through discussion. The results are presented in tabular, figure, and narrative formats.

RESULTS

Data were extracted from a total of 186 full-text publications. All of the studies, except for 1, were experimental. Included studies came from 33 countries, with the majority coming from the United States. Ex vivo methods were the predominant approach for extracting human tissue samples, and the most commonly studied tissue type was musculoskeletal. In this study, nearly 200 unique biomechanical values were reported, and the most commonly reported value was Young's (elastic) modulus. The most common type of mechanical test performed was tensile testing, and the most common reason for testing human tissues was to characterize biomechanical properties. Although the number of published studies on biomechanical properties of human tissues has increased over the past 20 years, there are many gaps in the literature. Of the 186 included studies, only 7 used human tissues for the design or validation of medical skills training devices. Furthermore, in studies where biomechanical values for human tissues have been obtained, a lack of standardization in engineering assumptions, methodologies, and tissue preparation may implicate the usefulness of these values.

CONCLUSIONS

This review is the first of its kind to give a broad overview of the biomechanics of human tissues in the published literature. With respect to high-fidelity haptics, there is a large gap in the published literature. Even in instances where biomechanical values are available, comparing or using these values is difficult. This is likely due to the lack of standardization in engineering assumptions, testing methodology, and reporting of the results. It is recommended that journals and experts in engineering fields conduct further research to investigate the feasibility of implementing reporting standards.

REVIEW REGISTRATION

Open Science Framework https://osf.io/fgb34.

Predicting Coronary Stenosis Progression Using Plaque Fatigue From IVUS-Based Thin-Slice Models: A Machine Learning Random Forest Approach

Introduction: Coronary stenosis due to atherosclerosis restricts blood flow. Stenosis progression would lead to increased clinical risk such as heart attack. Although many risk factors were found to contribute to atherosclerosis progression, factors associated with fatigue is underemphasized. Our goal is to investigate the relationship between fatigue and stenosis progression based on in vivo intravascular ultrasound (IVUS) images and finite element models.

Methods: Baseline and follow-up in vivo IVUS and angiography data were acquired from seven patients using Institutional Review Board approved protocols with informed consent obtained. Three hundred and five paired slices at baseline and follow-up were matched and used for plaque modeling and analysis. IVUS-based thin-slice models were constructed to obtain the coronary biomechanics and stress/strain amplitudes (stress/strain variations in one cardiac cycle) were used as the measurement of fatigue. The change of lumen area (DLA) from baseline to follow-up were calculated to measure stenosis progression. Nineteen morphological and biomechanical factors were extracted from 305 slices at baseline. Correlation analyses of these factors with DLA were performed. Random forest (RF) method was used to fit morphological and biomechanical factors at baseline to predict stenosis progression during follow-up.

Results: Significant correlations were found between stenosis progression and maximum stress amplitude, average stress amplitude and average strain amplitude (p < 0.05). After factors selection implemented by random forest (RF) method, eight morphological and biomechanical factors were selected for classification prediction of stenosis progression. Using eight factors including fatigue, the overall classification accuracy, sensitivity and specificity of stenosis progression prediction with RF method were 83.61%, 86.25% and 80.69%, respectively.

Conclusion: Fatigue correlated positively with stenosis progression. Factors associated with fatigue could contribute to better prediction for atherosclerosis progression.

Diffusion tensor imaging and arterial tissue: establishing the influence of arterial tissue microstructure on fractional anisotropy, mean diffusivity and tractography

This study investigates diffusion tensor imaging (DTI) for providing microstructural insight into changes in arterial tissue by exploring how cell, collagen and elastin content effect fractional anisotropy (FA), mean diffusivity (MD) and tractography. Five ex vivo porcine carotid artery models (n = 6 each) were compared-native, fixed native, collagen degraded, elastin degraded and decellularised. Vessels were imaged at 7 T using a DTI protocol with b = 0 and 800 s/mm2 and 10 isotopically distributed directions. FA and MD were evaluated in the vessel media and compared across models. FA values measured in native (p < 0.0001), fixed native (p < 0.0001) and collagen degraded (p = 0.0018, p = 0.0016, respectively) were significantly higher than those in elastin degraded and decellularised arteries. Native and fixed native had significantly lower MD values than elastin degraded (p < 0.0001) and decellularised tissue (p = 0.0032, p = 0.0003, respectively). Significantly lower MD was measured in collagen degraded compared with the elastin degraded model (p = 0.0001). Tractography yielded helically arranged tracts for native and collagen degraded vessels only. FA, MD and tractography were found to be highly sensitive to changes in the microstructural composition of arterial tissue, specifically pointing to cell, not collagen, content as the dominant source of the measured anisotropy in the vessel wall.