Tissue Viscoelasticity Research Articles

The Acute Effects of Theragun™ Percussive Therapy on Viscoelastic Tissue Dynamics and Hamstring Group Range of Motion

Handheld percussive therapy (PT) massage guns have seen a rapid rise in use and with-it increased attention within injury prevention and sport performance settings. Early studies have proposed beneficial effects upon range of motion (ROM), however the mechanism behind these increases remains unreported. This study aimed to determine the influence of a minimal frequency PT dose upon ROM and myotonometry outcomes. Twenty participants (N = 20; 13 males and 7 females, height 1.78cm ± 9.62; weight 77.35kg ± 8.46) participants were allocated to either a PT group receiving 2 x 60-seconds (plus 30-seconds rest) via a Theragun™ Pro4 to the hamstrings covering a standardised 20 lengths from proximal to distal via the standard ball attachment at 1 bar of pressure or a control group (CON) of 2-minutes 30-seconds passive supine rest. Pre and post intervention outcomes were measured for ROM via passive straight leg raise (PSLR) and tissue dynamics via MyotonPro (Tone, Stiffness, Elasticity, Relaxation Time). Results showed significant within-group increases (p < 0.0001, ηp2 0.656, +11.4%) in ROM following PT and between group difference against CON (P < 0.026). Significant within-group differences in stiffness (p < 0.016, ηp2 0.144, -6%), tone (p < 0.003, ηp2 0.213, +2%) and relaxation time (p < 0.002, ηp2 0.232, +6.3%) were also reported following PT. No significant difference was reported in elasticity (P > 0.05) or any other between group outcomes. PT therapy can provide an acute increase in hamstring group ROM following a resultant decrease in tissue stiffness.

The Acute Effects of Theragun™ Percussive Therapy on Viscoelastic Tissue Dynamics and Hamstring Group Range of Motion.

Handheld percussive therapy (PT) massage guns have seen a rapid rise in use and with-it increased attention within injury prevention and sport performance settings. Early studies have proposed beneficial effects upon range of motion (ROM), however the mechanism behind these increases remains unreported. This study aimed to determine the influence of a minimal frequency PT dose upon ROM and myotonometry outcomes. Twenty participants (N = 20; 13 males and 7 females, height 1.78cm ± 9.62; weight 77.35kg ± 8.46) participants were allocated to either a PT group receiving 2 x 60-seconds (plus 30-seconds rest) via a Theragun™ Pro4 to the hamstrings covering a standardised 20 lengths from proximal to distal via the standard ball attachment at 1 bar of pressure or a control group (CON) of 2-minutes 30-seconds passive supine rest. Pre and post intervention outcomes were measured for ROM via passive straight leg raise (PSLR) and tissue dynamics via MyotonPro (Tone, Stiffness, Elasticity, Relaxation Time). Results showed significant within-group increases (p < 0.0001, ηp2 0.656, +11.4%) in ROM following PT and between group difference against CON (P < 0.026). Significant within-group differences in stiffness (p < 0.016, ηp2 0.144, -6%), tone (p < 0.003, ηp2 0.213, +2%) and relaxation time (p < 0.002, ηp2 0.232, +6.3%) were also reported following PT. No significant difference was reported in elasticity (P > 0.05) or any other between group outcomes. PT therapy can provide an acute increase in hamstring group ROM following a resultant decrease in tissue stiffness.

Finite element formulation and implementation of largely deformable soft viscoelastic brain tissue responses

The impact forces due to accidental head impacts make the brain tissue distorted, twisted, and cause Traumatic Brain Injury (TBI). One way to predict the injury level is through simulations with appropriate material modeling and a computational framework. This work focuses on the Finite Element (FE) formulation of white matter responses to improve TBI predictions. Rate-dependent effects of white matter deformation are captured through the numerical implementation of an experimentally verified constitutive equation and nonlinear evolution equations of internal state variables. An ABAQUS UMAT is developed. One-element FE simulations are compared with the analytical and experimental results. Motivated by real-life scenarios, a few 3D Boundary Value Problems (BVPs) are solved to investigate the white matter responses.

Toward peripheral nerve mechanical characterization using Brillouin imaging spectroscopy.

Peripheral nerves are viscoelastic tissues with unique elastic characteristics. Imaging of peripheral nerve elasticity is important in medicine, particularly in the context of nerve injury and repair. Elasticity imaging techniques provide information about the mechanical properties of peripheral nerves, which can be useful in identifying areas of nerve damage or compression, as well as assessing the success of nerve repair procedures. We aim to assess the feasibility of Brillouin microspectroscopy for peripheral nerve imaging of elasticity, with the ultimate goal of developing a new diagnostic tool for peripheral nerve injury in vivo. Viscoelastic properties of the peripheral nerve were evaluated with Brillouin imaging spectroscopy. An external stress exerted on the fixed nerve resulted in a Brillouin shift. Quantification of the shift enabled correlation of the Brillouin parameters with nerve elastic properties. Brillouin microscopy provides sufficient sensitivity to assess viscoelastic properties of peripheral nerves.

Dynamic Hydrogels with Viscoelasticity and Tunable Stiffness for the Regulation of Cell Behavior and Fate.

The extracellular matrix (ECM) of natural cells typically exhibits dynamic mechanical properties (viscoelasticity and dynamic stiffness). The viscoelasticity and dynamic stiffness of the ECM play a crucial role in biological processes, such as tissue growth, development, physiology, and disease. Hydrogels with viscoelasticity and dynamic stiffness have recently been used to investigate the regulation of cell behavior and fate. This article first emphasizes the importance of tissue viscoelasticity and dynamic stiffness and provides an overview of characterization techniques at both macro- and microscale. Then, the viscoelastic hydrogels (crosslinked via ion bonding, hydrogen bonding, hydrophobic interactions, and supramolecular interactions) and dynamic stiffness hydrogels (softening, stiffening, and reversible stiffness) with different crosslinking strategies are summarized, along with the significant impact of viscoelasticity and dynamic stiffness on cell spreading, proliferation, migration, and differentiation in two-dimensional (2D) and three-dimensional (3D) cell cultures. Finally, the emerging trends in the development of dynamic mechanical hydrogels are discussed.

Active laminated-plate model for spontaneous bending of Hydra tissue fragments driven by supracellular actomyosin bundles

The outstanding regeneration of Hydra’s excised tissue fragments occurs via initial spontaneous bending to some quasi-stable shape in several minutes. However, the underlying mechanism and dynamics of such initial spontaneous bending are still debated. Here, we propose that the spontaneous bending is driven mechanically by supracellular actomyosin bundles inherited from parent Hydra. Our active laminated-plate (ALP) model predicts that the equilibrium fragment shape is determined by anisotropy in contractility and elasticity. We construct a minimal dynamic ALP model including three dissipation mechanisms. By variational analysis and bead-spring simulations, we find that the bending process starts diffusively from the edges and relaxes exponentially to the equilibrium shape. The different dissipation mechanisms take place at distinct timescales: the viscous drag occurs in 0.001 seconds, while the interlayer frictional sliding and cellular dissipation take place in minutes. The ALP model could be adapted to study multi-layered viscoelastic tissues with nematic cytoskeletal structures and provides a useful framework for tissue morphogenesis and regeneration.

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, to an advanced mechanics-based model, 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 0.60 kPa and 0.55 kPa. 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.

Gunshot wounds in parenchymatous organs: the morphology mainly depends on the physical properties of the affected tissues*

In contrast to gunshot wounds in skin and bone, the medico-legal literature pays little attention to the appearance of bullet penetration sites in abdominal organs. It was only in 1983 that Metter and Schulz published an article entitled “Morphological features of gunshot wounds in the liver and spleen.” According to their observations, the organs in question showed stellate tears at the bullet penetration sites resembling skin wounds from contact shots to body regions having a bony support. The study presented simulated the real conditions by means of test shots to composite models consisting of porcine organs embedded in ballistic gelatin. The ammunition used was pistol cartridges 9 mm Luger with full metal jacket round nose bullets. The shots were video-documented with a high-speed camera in order to record the bullet’s travel through the target. In addition, the composite models fired at underwent CT examinations followed by a macroscopic assessment of the organs. The study confirmed the findings of Metter and Schulz with regard to the star-like appearance of gunshot wounds in the liver and spleen. Likewise, the kidney showed radiating tears originating from the bullet path, whereas the wound track in pulmonary tissue was tube-shaped and lacked additional cracks. The varying wound patterns in parenchymatous organs can reasonably be explained as a consequence of the respective viscoelastic tissue properties.

Elementary biomechanics for the orthopaedic trainee

An understanding of biomechanics underpins our appreciation of the structure and function of the musculoskeletal system and informs clinical decisions and treatment choices. Since it is so fundamental it frequently features in end-of-training examinations in trauma and orthopaedic surgery, often in questions testing higher order thinking in which knowledge of the basics is assumed and it is the application of these principles that is the focus. This article arms the trainee with these basics, starting with the application of Newtonian principles to statics and dynamics, applying these and then introducing material properties including the viscoelastic tissues that build the musculoskeletal system.

Bubble pulsation characteristics in multi-bubble systems affected by bubble size polydispersity and spatial structure

This study seeks to explore the bubble pulsation characteristics in multi-bubble environment with a special focus on the influences of the size polydispersity and the two-dimensional structure of bubbles. Three representative configurations of three interacting bubbles are formed by setting the initial radii of cavitation bubbles and inter-bubble distances appropriately, then the pulsation characteristics of a small bubble are investigated and compared by the bifurcation analysis. The results illustrate that the bubble size polydispersity and two-dimensional structure would greatly affect the bubble pulsations (i.e., the amplitude and nonlinearity of pulsations). Furthermore, the effects of two-dimensional structure are strong at a small inter-bubble distance of the large and small bubbles while the bubble size polydispersity always significantly affects the bubble pulsations for all cases. Moreover, the influences of both bubble size polydispersity and two-dimensional structure can be enhanced as the acoustic pressure increases, which can also become stronger when the large bubble is located at the same side as the small bubble and the initial radius of large bubble increases. Additionally, the effects would also be increased when the tissue viscoelasticity varies within a certain range. The present findings shed new light on the dynamics of multiple polydisperse microbubbles in viscoelastic tissues, potentially contributing to an optimization of their applications with ultrasound excitation.

Quasi-dynamic breathing model of the lung incorporating viscoelasticity of the lung tissue.

We advanced a novel model to calculate viscoelastic lung compliance and airflow resistance in presence of mucus, accounting for the quasi-linear viscoelastic stress-strain response of the parenchyma (alveoli) tissue. We adapted a continuum-based numerical modeling approach for the lung, integrating the fluid mechanics of the airflow within individual generations of the bronchi and alveoli. The model accounts for elasticity of the deformable bronchioles, resistance to airflow due to the presence of mucus within the bronchioles, and subsequent mucus flow. Simulated quasi-dynamic inhalation and expiration cycles were used to characterize the net compliance and resistance of the lung, considering the rheology of the mucus and viscoelastic properties of the parenchyma tissue. The structure and material properties of the lung were identified to have an important contribution to the lung compliance and airflow resistance. The secondary objective of this work was to assess whether a higher frequency and smaller volume of harmonic air flow rate compared to a normal ventilator breathing cycle enhanced mucus outflow. Results predict, lower mucus viscosity and higher excitation frequency of breathing are favorable for the flow of mucus up the bronchi tree, towards the trachea.

Creep deformation of viscoelastic lumbar tissue during sustained submaximal trunk flexion postures

Most in-vivo human experiments exploring creep deformation of viscoelastic lumbar tissue have used a maximum trunk flexion posture to engage the lumbar passive tissues. Recent evidence suggests that static trunk flexion tasks requiring submaximal trunk flexion can lead to gradual changes in the lumbar lordosis and this leads to our hypothesis that maintaining submaximal trunk flexion postures may lead to significant creep deformation of the viscoelastic lumbar tissues. Sixteen participants maintained a trunk flexion posture that was ten degrees less than the trunk flexion posture eliciting flexion-relaxation phenomenon for 12 min with breaks for a maximal trunk flexion protocol every three minutes. Trunk kinematic and extensor EMG measures were captured during the static, submaximal trunk flexion protocol as well as during the maximal trunk flexion protocol to provide evidence of creep development in the lumbar passive tissues. Results revealed that 12-minutes of submaximal trunk flexion led to significant increases in peak lumbar flexion angle (1.3°) and EMG-off lumbar flexion angle for L3/L4 paraspinals (2.9°). During the submaximal trunk flexion protocol, the changes in the lumbar flexion angle at 3–6 min and 6–9 min (average Δ5.4°) were significantly greater than at 0–3 min (Δ2.0°). The contribution of this study is the demonstration that sustained submaximal trunk flexion posture (i.e., constant global system) can lead to creep deformation of the viscoelastic lumbar tissue due to the increased lumbar flexion (i.e., altered local system) and may be attributed to a reduction in lumbar lordosis as the extensor muscles fatigue.

The role of shear viscosity as a biomarker for improving chronic kidney disease detection using shear wave elastography: A computational study using a validated finite element model

The application of ultrasound shear wave elastography for detecting chronic kidney disease, namely renal fibrosis, has been widely studied. A good correlation between tissue Young’s modulus and the degree of renal impairment has been established. However, the current limitation of this imaging modality pertains to the linear elastic assumption used in quantifying the stiffness of renal tissue in commercial shear wave elastography systems. As such, when underlying medical conditions such as acquired cystic kidney disease, which may potentially influence the viscous component of renal tissue, is present concurrently with renal fibrosis, the accuracy of the imaging modality in detecting chronic kidney disease may be affected. The findings in this study demonstrate that quantifying the stiffness of linear viscoelastic tissue using an approach similar to those implemented in commercial shear wave elastography systems led to percentage errors as high as 87%. The findings presented indicate that use of shear viscosity to detect changes in renal impairment led to a reduction in percentage error to values as low as 0.3%. For cases in which renal tissue was affected by multiple medical conditions, shear viscosity was found to be a good indicator in gauging the reliability of the Young’s modulus (quantified through a shear wave dispersion analysis) in detecting chronic kidney disease. The findings show that percentage error in stiffness quantification can be reduced to as low as 0.6%. The present study demonstrates the potential use of renal shear viscosity as a biomarker to improve the detection of chronic kidney disease.

Principles of Tendon Structure, Healing, and the Microenvironment.

Tendon is a strong viscoelastic tissue, responsible for conducting force from muscle to bone. In the hand, flexor tendons course through fibro-osseous sheaths, composed of an intricate tenosynovium and fibrocartilaginous pulley system. After flexor tendon laceration, changes occur in tendon force transduction as well as vascularity, affecting tendon healing on a tissue and cellular level. Tendon healing occurs through intrinsic and extrinsic mechanisms, which in combination with local anatomy, can predispose to adhesion formation. Understanding the relationship between microenvironmental cues and tendon healing on the cellular and tissue level will improve our knowledge and treatment of flexor tendon injuries.

Highly accurate and reliable ultrasonic focusing capability in heterogeneous media using a spherical cavity transducer

Introduction: Focused ultrasound ablation surgery (FUAS) has been emerging to treat a wide range of conditions non-invasively and effectively with promising therapeutic outcomes. The focusing capability of an ultrasound transducer (i.e., focus shift, beam distortion, and acoustic pressure at the focus) determines the ablation effects. However, the focus shift and focal beam distortion after ultrasound propagating through multi-layered heterogeneous viscoelastic biological tissues become significant and are found to deteriorate the performance of FUAS in clinics.Methods: To achieve an accurate and reliable focal field among patients with large variations in the anatomical structures and properties, a spherical cavity transducer with open ends and sub-wavelength focal size (Li et al., APL, 2013,102:204102) was applied here. Both experimental measurements and numerical simulations were performed to characterize the acoustic fields of the spherical cavity transducer in water, the multi-layered concentric cylindrical phantom, and the heterogeneous tissue model (an adult male pelvis enclosed by porcine skin, fat, and muscle) and then compared with those of a conventional concave transducer at the same electrical power output.Results: It is found that standing-wave focusing using the spherical cavity transducer results in much less focus shift (0.25λ vs. 1.67λ) along the transducer axis and focal beam distortion (−6 dB beam area of 0.71 mm2vs. 4.72 mm2 in water and 2.55 mm2vs. 17.30 mm2 in tissue) in the focal plane but higher pressure focusing gain (40.05 dB vs. 33.61 dB in tissue).Discussion: Such a highly accurate and reliable focal field is due to the excitation at an appropriate eigen-frequency of the spherical cavity with the varied media inside rather than the reverberation from the concave surface. Together with its sub-wavelength focal size, the spherical cavity transducer is technically advantageous in comparison to the concave one. The improved focusing capability would benefit ultrasound exposure for not only safer and more effective FUAS in clinics, but also broad acoustic applications.

Linear viscoelasticity of human sclera and posterior ocular tissues during tensile creep

PurposeDespite presumed relevance to ocular diseases, the viscoelastic properties of the posterior human eye have not been evaluated in detail. We performed creep testing to characterize the viscoelastic properties of ocular regions, including the sclera, optic nerve (ON) and ON sheath. MethodsWe tested 10 pairs of postmortem human eyes of average age 77 ± 17 years, consisting of 5 males and 5 females. Except for the ON that was tested in native shape, tissues were trimmed into rectangles. With physiologic temperature and constant wetting, tissues were rapidly loaded to tensile stress that was maintained by servo feedback as length was monitored for 1,500 sec. Relaxation modulus was computed using Prony series, and Deborah numbers estimated for times scales of physiological eye movements. ResultsCorrelation between creep rate and applied stress level was negligible for all tissues, permitting description as linear viscoelastic materials characterized by lumped parameter compliance equations for limiting behaviors. The ON was the most compliant, and anterior sclera least compliant, with similar intermediate values for posterior sclera and ON sheath. Sensitivity analysis demonstrated that linear behavior eventually become dominant after long time. For the range of typical pursuit tracking, all tissues exhibit Debora numbers less than 75, and should be regarded as viscoelastic. With a 6.7 Deborah number, this is especially so for the ON during pursuit and convergence. ConclusionsPosterior ocular tissues exhibit creep consistent with linear viscoelasticity necessary for describing biomechanical behavior of the ON, its sheath, and sclera during physiological eye movements and eccentric ocular fixations.Running Head: Tensile Creep of Human Ocular Tissues.

Real-time simulation of viscoelastic tissue behavior with physics-guided deep learning.

Finite element methods (FEM) are popular approaches for simulation of soft tissues with elastic or viscoelastic behavior. However, their usage in real-time applications, such as in virtual reality surgical training, is limited by computational cost. In this application scenario, which typically involves transportable simulators, the computing hardware severely constrains the size or the level of details of the simulated scene. To address this limitation, data-driven approaches have been suggested to simulate mechanical deformations by learning the mapping rules from FEM generated datasets. Prior data-driven approaches have ignored the physical laws of the underlying engineering problem and have consequently been restricted to simulation cases of simple hyperelastic materials where the temporal variations were effectively ignored. However, most surgical training scenarios require more complex hyperelastic models to deal with the viscoelastic properties of tissues. This type of material exhibits both viscous and elastic behaviors when subjected to external force, requiring the implementation of time-dependant state variables. Herein, we propose a deep learning method for predicting displacement fields of soft tissues with viscoelastic properties. The main contribution of this work is the use of a physics-guided loss function for the optimization of the deep learning model parameters. The proposed deep learning model is based on convolutional (CNN) and recurrent layers (LSTM) to predict spatiotemporal variations. It is augmented with a mass conservation law in the lost function to prevent the generation of physically inconsistent results. The deep learning model is trained on a set of FEM datasets that are generated from a commercially available state-of-the-art numerical neurosurgery simulator. The use of the physics-guided loss function in a deep learning model has led to a better generalization in the prediction of deformations in unseen simulation cases. Moreover, the proposed method achieves a better accuracy over the conventional CNN models, where improvements were observed in unseen tissue from 8% to 30% depending on the magnitude of external forces. It is hoped that the present investigation will help in filling the gap in applying deep learning in virtual reality simulators, hence improving their computational performance (compared to FEM simulations) and ultimately their usefulness.

Engineering viscoelastic alginate hydrogels for hiPSC cardiomyocyte culture.

Heart disease is the leading cause of death worldwide, however research progress of heritable cardiomyopathies has been limited by the lack of cost effective and accurate human models. Human induced pluripotent stem cells (hiPSCs) are a renewable cell source that can be differentiated into human cardiomyocytes (CMs), presenting a reliable tool for in vitro cellular models. The physiological environmental cues that CMs are exposed to are extraordinarily complex and in vitro systems typically fail to mimic key aspects such as viscoelasticity and adhesion ligand type. Viscoelasticity in particular is important to model as it is exhibited by virtually all tissues in the body and can influence several cell functions including spreading, differentiation and proliferation. However, the majority of experiments with CMs to date have used covalently crosslinked hydrogels that do not exhibit any stress relaxing properties. Here, we engineered biomaterials to recapitulate these viscoelastic heart tissue characteristics. Since the stress relaxation properties of heart tissue have not been directly measured, we first mechanically characterized left ventricular porcine tissue. The elastic modulus was around 7 kPa, within range of reported values. However, the tissue exhibited much faster stress relaxation values than we anticipated; relaxing to half its max value in less than 10 s while skeletal muscle tissue is reported to relax closer to 100 s. We synthesized an alginate hydrogel with conjugated PEG chains to mimic these fast-relaxing properties while maintaining similar elastic modulus values. We then seeded CMs on the hydrogels, imaged and analyzed the structure and function via confocal microscopy to assess cell adherence and morphology. These studies have validated our engineered hydrogel system as a hiPSC-CM culture platform and model system to investigate how viscoelasticity influences iPSC-CM function in wild-type and disease cell lines.

On the thermo-vibrational response of multi-layer viscoelastic skin tissue to laser irradiation

This paper investigates temperature and vibration responses of human multi-layer skin tissue to laser irradiation. Due to the viscoelastic property of skin, a standard linear solid model known as the Zener model with two relaxation times with dual phase-lag heat conduction model has been used to describe the temperature and displacement in a one-dimensional form. The obtained differential equations have been discretized in the weak form to balance thermal and vibrational energies. The extracted time-dependent ordinary differential equations have been solved numerically by integration over time. The investigations are based on the effect of four relaxation times of temperature, heat, strain, and stress with laser effect in the form of a single step or repeated pulses on the behavior of the skin. By examining their results, the effect of phase lags has also been discussed, showing that the heat phase lag has a complex impact on thermal response and depends on the type of laser radiation. The phase lag of temperature reduces the temperature change rate, and the strain phase lag spreads the displacement at later times, gives inertia to the system, and dampens the system's vibration.

Fractional Dual-Phase-Lag Model for Nonlinear Viscoelastic Soft Tissues

The primary goal of this paper is to create a new fractional boundary element method (BEM) model for bio-thermomechanical problems in functionally graded anisotropic (FGA) nonlinear viscoelastic soft tissues. The governing equations of bio-thermomechanical problems are briefly presented, including the fractional dual-phase-lag (DPL) bioheat model and Biot’s model. The more complex shapes of nonlinear viscoelastic soft tissues can be handled by the boundary element method, which also avoids the need for the interior domain to be discretized. The fractional dual-phase-lag bioheat equation was solved using the general boundary element method (GBEM) based on the local radial basis function collocation method (LRBFCM). The poroelastic fields are then calculated using the convolution quadrature boundary element method (CQBEM) The numerical findings show that our proposed numerical model is valid, efficient, and accurate.