Temperature Distribution In Tissue Research Articles

Numerical simulation of heat transfer properties of skin tissue acted on repetitive laser irradiation

Pulsed laser has been widely used in the clinical treatment of various diseases and injuries due to its unique advantages in the process of interaction with skin tissue. In order to ensure the therapeutic effect and patient safety, it is necessary to accurately monitor the time-space response of the whole-field temperature of the target tissue irradiated by pulsed laser. A research strategy to explore the thermal response and incidental thermal damage of skin tissue induced by repeated pulses laser has been proposed in present work. A theoretical model involved variable physical properties was first proposed, in which the dual-phase lag (DPL) equation and Henrique burn equation were employed to evaluate the tissue temperature and thermal damage induced by repeated pulse laser irradiation. With the help of finite difference method, the governing equation with variable physical properties was then numerical solved to obtain the time-space distribution of tissue temperature, and the evolution process of the thermal damage was further calculated. The effects of laser input parameters and variable physical parameters on the laser penetration depth, temperature distribution and burn degree of the irradiated tissue were analyzed.

Analytical solution of three-dimensional temperature field for skin tissue considering blood perfusion rates under laser irradiation and thermal damage analysis

An analytical solution of the three-dimensional transient temperature distribution for skin tissue when heated by a laser heat source is presented in this work. An equation for the three-dimensional bioheat transfer of skin tissue is obtained firstly, and the transient temperature value at any point inside the skin tissue can be solved using the separation variable method and the Newton-Cotes method. Previous research primarily focused on semi-infinite domains, while this work presents an analytical solution for finite domains. A comparison of present analytical solution with simulation software results and numerical solution is given to demonstrate the feasibility of the analytical solution. The three-dimensional temperature distribution of skin tissue irradiated by a laser is theoretically studied. The extent of thermal damage to skin tissue is assessed by substituting the analytical solution of the temperature field for laser-irradiated skin tissue into the thermal damage equation. The calculations in this work reveal that the variation of temperature field in biological tissues is dependent not only on the laser incident light intensity and the optical parameters of the tissues but also on the heat transfer properties of the biological tissues, such as the blood perfusion rates.

Asymptotic solution of electromagnetic heating of skin tissue with lateral heat conduction

We study the temperature evolution in the three-dimensional skin tissue exposed to an electromagnetic beam of millimeter wavelength. The skin absorption coefficient of the beam frequency determines how deep the electromagnetic energy penetrates into the skin tissue, which gives a sub-millimeter penetration depth for a 94 GHz wave. In contrast, in the lateral directions perpendicular to the depth, the beam size is usually much larger than the penetration depth. Based on this separation of length scales, we establish an asymptotic formulation in which each term has separable dependences on the depth coordinate and on the lateral coordinates. We solve it analytically to obtain a two-term asymptotic solution of the temperature distribution in the three-dimensional skin tissue. This closed-form analytical solution provides a practical and accurate way of predicting the temperature. When the beam size is moderately larger than the penetration depth (a ratio of 20), the effect of lateral heat conduction is well captured in the asymptotic solution with maximum error less than 0.0017 in the normalized temperature of magnitude well above 1.

Mechanics and thermal analyses of microfluidic nerve-cooler system

Recent advances in microelectronics and biomedical technology has led to capabilities for integrating programmable cooling mechanisms into bioelectronic devices for pain management and other important applications in human health. However, achieving localized and precise targeted cooling remains a challenge due to the influence of tissue mechanics during device placement/operation, viscous effects in the flow of coolants, and critical physical parameters that locally affect tissue temperature and mechanical responses. Recent studies demonstrate that bioelectronic devices can be equipped with microfluidic structures designed to support phase-change cooling mechanisms, with cooling power adjustable by control of fluid flow rates. When operated in closed-loop feedback schemes based on measurements of temperature at the tissue interface, these platforms can achieve precise temperature control of regions of peripheral nerves involved in pain signaling. Establishing a theoretical model to understand the effects of these devices on the underlying soft and deformable tissues, along with microfluidic deformations that can alter the heat transfer cooling process in viscous flow, is critical for optimized design and operation. To address this, a theoretical model is developed to describe the influence of remote strain in the tissue and to quantify the spatial temperature distribution of tissue cooled by fluid phase change in deformable microfluidic channels. A scaling law is derived to quantify the steady-state temperature distribution in the tissue under physiologically relevant mechanical loads, fluid viscosity, flow rates, and thermal conductivities, enabling the prediction of liquid and gas flux based on expected temperature changes and saturated vapor pressure in the deformed tissue and microchannel. These findings reveal that within a reasonable physiological range, the influence of fluid viscosity on temperature changes can be neglected, and the ratio of fluid to tissue thermal conductivity minimally impacts the temperature change at relatively high flow rates. Additionally, the model suggests a limit to tissue cooling for a given fluid, which may not necessarily increase with higher fluid volumes.

Using phase change material nanoparticles for thermal storage to protect healthy tissues by hyperthermia method assisted by a magnetic field for cancer tissue ablation

Magnetic fluid hyperthermia (MFH) is a promising cancer treatment due to its minimal invasiveness and limited adverse effects. It targets tumor cells by heating them to a specific temperature, utilizing heat generated by magnetic nanoparticles (MNPs) in response to an external magnetic field. This study is a numerical theoretical research, in which tissue cancer is embedded with MNPs and healthy tissue is embedded with phase change materials (PCMs). Among different materials, PCM paraffin wax is selected because it is recognized as one of the most functional types of paraffin-based PCM, known for its high thermal storage capability, and is a also subset of non-toxic organic PCM materials. The novelty of this work lies in using a magnetic field produced by a solenoid to approach real conditions and using phase change materials as energy storage materials to protect healthy cells. The finite element method (FEM) by COMSOL Multi-physics commercial software is used to solve governing equations. The effect of using PCMs on tissue temperature distribution is carefully studied. The results of this study show that the use of PCM significantly reduces the temperature of healthy tissues around the tumor in a controllable temperature range, leading to less tissue damage when MNPs release heat. The effect of PCMs on tissue temperature distribution increases with the rise of their volume fraction. The results show that moving the tumor from the center to the outside of the coil decreases the temperature inside the tumor, and the temperature distribution becomes non-uniform. Results from this study can be used to add PCM for MFH to control the temperature of tissue more accurately.

Comparative analysis of non-invasive and invasive alternating electric fields therapy for malignant gliomas: a simulation study

Alternating electric fields (AEFs) at intermediate frequencies (100–300 kHz) and low intensities (1–3 V/cm) have shown promise as an effective approach for inhibiting cancer cell proliferation. However, a noticeable research gap exists in comparing the biophysical properties of invasive and non-invasive AEFs methods, and AEFs delivery strategies require further improvement. In this study, we constructed a realistic head model to simulate the effects of non-invasive and invasive AEFs on malignant gliomas. Additionally, a novel method was proposed involving the placement of a return electrode under the scalp. We simulated the electric field and temperature distributions in the brain tissue for each method. Our results underscore the advantages of invasive AEFs, showcasing their superior tumor-targeting abilities and reduced energy requirements. The analysis of brain tissue temperature changes reveals that non-invasive AEFs primarily generate heat at the scalp level, whereas invasive methods localize heat production within the tumor itself, thereby preserving surrounding healthy brain tissue. Our proposed invasive AEFs method also shows potential for selective electric field intervention. In conclusion, invasive AEFs demonstrate potential for precise and effective tumor treatment. Its enhanced targeting capabilities and limited impact on healthy tissue make it a promising avenue for further research in the realm of cancer treatment.

Patient-specific temperature distribution prediction in laser interstitial thermal therapy: single-irradiation data-driven method

Laser interstitial thermal therapy (LITT) is popular for treating brain tumours and epilepsy. The strict control of tissue thermal damage extent is crucial for LITT. Temperature prediction is useful for predicting thermal damage extent. Accurately predicting in vivo brain tissue temperature is challenging due to the temperature dependence and the individual variations in tissue properties. Considering these factors is essential for improving the temperature prediction accuracy. Objective. To present a method for predicting patient-specific tissue temperature distribution within a target lesion area in the brain during LITT. Approach. A magnetic resonance temperature imaging (MRTI) data-driven estimation model was constructed and combined with a modified Pennes bioheat transfer equation (PBHE) to predict patient-specific temperature distribution. In the PBHE for temperature prediction, the individual specificity and temperature dependence of thermal tissue properties and blood perfusion, as well as the individual specificity of optical tissue properties were considered. Only MRTI data during one laser irradiation were required in the method. This enables the prediction of patient-specific temperature distribution and the resulting thermal damage region for subsequent ablations. Main results. Patient-specific temperature prediction was evaluated based on clinical data acquired during LITT in the brain, using intraoperative MRTI data as the reference standard. Our method significantly improved the prediction performance of temperature distribution and thermal damage region. The average root mean square error was decreased by 69.54%, the average intraclass correlation coefficient was increased by 37.5%, the average Dice similarity coefficient was increased by 43.14% for thermal damage region prediction. Significance. The proposed method can predict temperature distribution and thermal damage region at an individual patient level during LITT, providing a promising approach to assist in patient-specific treatment planning for LITT in the brain.

Review of the Capacity to Accurately Detect the Temperature of Human Skin Tissue Using the Microwave Radiation Method.

Microwave radiometry (MWR) is instrumental in detecting thermal variations in skin tissue before anatomical changes occur, proving particularly beneficial in the early diagnosis of cancer and inflammation. This study concisely traces the evolution of microwave radiometers within the medical sector. By analyzing a plethora of pertinent studies and contrasting their strengths, weaknesses, and performance metrics, this research identifies the primary factors limiting temperature measurement accuracy. The review establishes the critical technologies necessary to overcome these limitations, examines the current state and prospective advancements of each technology, and proposes comprehensive implementation strategies. The discussion elucidates that the precise measurement of human surface and subcutaneous tissue temperatures using an MWR system is a complex challenge, necessitating an integration of antenna directionality for temperature measurement, radiometer error correction, hardware configuration, and the calibration and precision of a multilayer tissue forward and inversion method. This study delves into the pivotal technologies for non-invasive human tissue temperature monitoring in the microwave frequency range, offering an effective approach for the precise assessment of human epidermal and subcutaneous temperatures, and develops a non-contact microwave protocol for gauging subcutaneous tissue temperature distribution. It is anticipated that mass-produced measurement systems will deliver substantial economic and societal benefits.

Enhancement of skin tumor laser hyperthermia with Ytterbium nanoparticles: numerical simulation

Laser hyperthermia therapy (HT) has emerged as a well-established method for treating cancer, yet it poses unique challenges in comprehending heat transfer dynamics within both healthy and cancerous tissues due to their intricate nature. This study investigates laser HT therapy as a promising avenue for addressing skin cancer. Employing two distinct near-infrared (NIR) laser beams at 980 nm, we analyze temperature variations within tumors, employing Pennes’ bioheat transfer equation as our fundamental investigative framework. Furthermore, our study delves into the influence of Ytterbium nanoparticles (YbNPs) on predicting temperature distributions in healthy and cancerous skin tissues. Our findings reveal that the application of YbNPs using a Gaussian beam shape results in a notable maximum temperature increase of 5 °C within the tumor compared to nanoparticle-free heating. Similarly, utilizing a flat top beam alongside YbNPs induces a temperature rise of 3 °C. While this research provides valuable insights into utilizing YbNPs with a Gaussian laser beam configuration for skin cancer treatment, a more thorough understanding could be attained through additional details on experimental parameters such as setup, exposure duration, and specific implications for skin cancer therapy.

Closed-loop high precision human tissue temperature measurements using a joint forward and inverse method

A closed-loop forward-inverse joint multi-layer temperature prediction method is introduced to overcome the limitations of non-contact microwave-based precise temperature measurements within human tissues. This approach merges an incoherent four-layer tissue forward model with a multi-frequency high-precision inversion algorithm. Random errors were incorporated into the forward model to construct a multi-layer human tissue dataset for performance validation and optimization of the inversion algorithm. The lack of precise temperature measurements in internal human tissues was addressed using an incoherent four-layer forward model that integrates the Pennes heat transfer equation with a fluid–solid coupling boundary modeling technique. Parameter differentiation analysis was conducted in the forward modeling step using incoherent electromagnetic transport equations. The proposed high-precision multi-frequency inversion process, added to the objective function, refines the XGBoost algorithm by assigning a penalty factor and adjusting for neighboring tissue temperature distributions. The Optuna framework was then utilized to optimize XGBoost hyper-parameter sets, resulting in the Opt-XGBoost inversion algorithm. This approach achieved a root mean square error of 0.033 °C and an average absolute error of 0.0256 °C in simulations involving forward modeling-generated data.

Non-Fourier Bioheat Transfer Analysis in Brain Tissue During Interstitial Laser Ablation: Analysis of Multiple Influential Factors

This work presents the dual-phase lag-based non-Fourier bioheat transfer model of brain tissue subjected to interstitial laser ablation. The finite element method has been utilized to predict the brain tissue's temperature distributions and ablation volumes. A sensitivity analysis has been conducted to quantify the effect of variations in the input laser power, treatment time, laser fiber diameter, laser wavelength, and non-Fourier phase lags. Notably, in this work, the temperature-dependent thermal properties of brain tissue have been considered. The developed model has been validated by comparing the temperature obtained from the numerical and ex vivo brain tissue during interstitial laser ablation. The ex vivo brain model has been further extended to in vivo settings by incorporating the blood perfusion effects. The results of the systematic analysis highlight the importance of considering temperature-dependent thermal properties of the brain tissue, non-Fourier behavior, and microvascular perfusion effects in the computational models for accurate predictions of the treatment outcomes during interstitial laser ablation, thereby minimizing the damage to surrounding healthy tissue. The developed model and parametric analysis reported in this study would assist in a more accurate and precise prediction of the temperature distribution, thus allowing to optimize the thermal dosage during laser therapy in the brain.

MODELING OF TEMPERATURE GRADIENTS DURING THE INTERACTION OF LASER RADIATION WITH BIOLOGICAL TISSUE

Modeling thermal gradients in biological tissues, when interacting with laser radiation, helps to understand how heat flows are distributed in the body and reflect heating zones. The main aspects of the simulation include the parameters of the laser source, such as the wavelength and power, the initial temperature conditions of the tissues, as well as the use of special numerical methods that help to optimally solve the heat conduction equations and visualize the obtained results. Temperature distribution in tissues plays an important role in determining the effectiveness and safety of laser treatment. This allows you to choose effective parameters of laser radiation and irradiation modes to achieve the desired result without negative consequences for the patient. However, it is important to consider that the actual behavior of tissues in response to laser radiation may be more complex due to the physiological characteristics of each person.Therefore, all models and calculations must take into account this feature and diversity and take into account safety regulations. Laser treatment should be carried out under the supervision of qualified specialists, in particular doctors who have relevant knowledge and experience in the field of medicine. This approach helps ensure the safety and effectiveness of laser procedures for patients. Modeling the distribution of tissue thermal gradients when interacting with laser radiation plays an important role in the development of modern medicine and scientific research and provides efficiency compared to the obtained experimental data. It helps to improve the understanding of thermal processes in biological tissues and to ensure the safety of patients receiving laser treatment. In this work, experiments were conducted on the effect of laser irradiation on biological tissue, as well as modeling of changes in temperature gradients during the effect of laser irradiation on biological tissue, using various models of heat and mass transfer, using the Matlab software environment. As a result of the modeling in this work, the patterns of changes in temperature gradients were obtained, which were compared with the experimentally obtained data, the errors of Pennes models and finite element methods, finite differences were analyzed, and conclusions were drawn.

Effect of geometric parameters of electrodes on skin heating for the design of non-ablative radiofrequency device.

Non-ablative radiofrequency (RF) has been widely used in clinical and at-home cosmetics devices. RF electrode geometry can influence the heat distribution in the tissue. This study analyzes the influence of geometric parameters of the electrode on the heat distribution in the layered tissue. The finite element simulation of the electrothermal coupling field was performed to obtain the three-dimensional (3D) temperature distribution of the four-layer tissue. The electrode geometric parameters including the inter-electrode spacing (5-12mm), width (1-3mm), length (3-10mm), shapes (bar, dot and circle), and the coupling gel's electrical conductivity (0.2-1.5 S/m) were simulated. The maximum temperature at 2mm depth (T-2mm ) and the temperature difference (Tdiff ) between the maximum skin surface temperature and T-2mm were obtained to evaluate the effectiveness and safety. The effect of geometric parameters on the effectiveness and safety was mixed. The maximum T-2mm occurred with the 5mm inter-electrode spacing, 3mm width, 10mm length, the circle-shaped electrode, and the 1.5 S/m coupling gel's electrical conductivity. The ratio of inter-electrode spacing to width at around four can achieve rapid temperature rise and skin surface temperature protection. The electrode shape influenced the area of temperature rise in the tissue's cross-section. The coupling gel's electrical conductivity should be close to that of the skin to avoid energy accumulation on the skin surface. The electrode's geometric parameters affect the effectiveness and safety of the RF product. This study has provided the simulation procedure for the electrode design.

Modified Pennes bioheat equation with heterogeneous blood perfusion: A newer perspective

The Pennes bioheat equation is a widely used mathematical model for predicting temperature distribution in biological tissues. However, it assumes homogeneous blood perfusion throughout the tissue, which may not accurately represent the complex perfusion patterns observed in tumors. In this study, we propose modification to traditional Pennes bioheat equation that incorporates the effects of perfusion heterogeneity by introducing a spatially varying perfusion term, represented by a perfusion coefficient, ωb(x,y,z) to account for local variations in blood flow at each spatial location within the biological tissue. Therefore, heterogeneous blood perfusion at each tumor tissue voxel location relax the uniform blood perfusion assumption. By incorporating these modifications, the model allows for a more realistic representation of the heat exchange between the biological tissue and blood flow, by predicting the non-uniform temperature distribution particularly in tumors with irregular blood vessel growth and organization, thereby facilitating treatment planning and optimization of thermal therapies, and leading to more effective therapeutic outcomes. The spatially varying perfusion coefficient can be obtained through advanced imaging techniques such as dynamic contrast-enhanced MRI or Doppler ultrasound enabling the assessment of perfusion heterogeneity in biological tissues. In conclusion, the modified Pennes bioheat equation offers a valuable tool for understanding the intricate relationship between with heterogeneous blood perfusion and temperature distribution in biological tissues, providing important insights to the thermal response of tumors and guiding the development of improved therapeutic strategies in optimizing cancer treatments.

Physics-informed neural network for fast prediction of temperature distributions in cancerous breasts as a potential efficient portable AI-based diagnostic tool

This work presents the development of a novel Physics-Informed Neural Network (PINN) method for fast forward simulation of heat transfer through cancerous breast models. The proposed PINN method combines deep learning and physical principles to predict the temperature distributions in breast tissues and identify potential abnormal regions indicating the presence of tumors. The PINN model is normally trained by physics in terms of the residuals of the heat transfer equation, as well as boundary conditions with and without datasets of surface thermal imaging data concerning cancerous breast tissues, which can be used for future inverse thermal modeling to calculate tumor sizes and locations. The model is validated by comparing its predictions with those obtained by traditional Finite Element Analysis (FEA) for various cases. The comparison validates the PINN model as an accurate and fast method for thermal modeling and breast cancer diagnostic tool as the PINN simulation is found to be around 12 times faster than its FEM counterpart. The utilization of deep learning and physical principles in a diagnostic tool provides a non-invasive and safer alternative for breast self-examination compared to traditional methods such as mammography. These findings hold promise for the ongoing development of a new portable Artificial Intelligence (AI) tool for the early detection of breast cancer in breast self-examination as promoted by WHO, which is crucial for reducing mortality rates of breast cancer in the world.

Temperature distribution in tumour tissue during targeted destruction by heat: A hyperbolic bioheat equation approach

Thermotherapy is a frequently used treatment to destroy malignant tumour. The procedure is carried-out using various techniques like radio frequency, laser, focused ultrasound, and microwaves to elevate the desired temperature at targeted tissues. However, it becomes indispensable to note that the impact of the high temperatures also expands to the peripherals of the targeted tissues and can harm the surrounding healthy tissue. This paper aims to find the temperature distribution in tumour tissue during the process and draw the corresponding graphical representation. A five-tine trocar has used to raise the temperature at the centre of the targeted tissue as much as to destroy the tumour tissue. A mathematical model based on the hyperbolic bioheat equation has been formulated and solved by variable separable method after converting the devised equation into dimensionless form. The heat waves make a significant contribution in the propagation of heat at high temperatures therefore, this paper can be viewed as an improvement of the models based on parabolic Penne’s bioheat equation. The results are helpful in estimating the temperature distribution on and around the targeted tissue and hence the therapist can be guided to prevent injuries during the treatment of therapeutic treatments.

Simulation study of mobile phone radiation thermal effect in human heart tissue

The public's concern over potential health risks related to the absorption of electromagnetic radiation (EMR) has been developing due to the prolonged use of cell phones close to the human body, particularly the human heart. To address these issues, this study aims to present a numerical simulation of EMR in the spectral range (900 MHz, 1800 MHz, and 2400 MHz) on human heart tissue using a Matlab program and the Finite-Difference Time-Domain (FDTD) method in One Dimension (1D). A mathematical analysis of electromagnetic radiation heating equations in a one-dimensional, one-layer model has also been discussed by numerically calculating the transient bioheat transfer equation and Maxwell's equations by using the FDTD to predict the effects of thermal physics properties on the transient temperature of human heart tissue. The results revealed that FDTD is an efficient method to evaluate the thermal effect of EMR due to its perfect boundary condition. It was found that the heart tissue reacts more at 900 MHz compared to 1800 MHz and 2400 MHz, and the tissue absorption is higher at the lower frequency. The effects of various parameters on the temperature increase in the human heart tissue were considered, such as the electric field, magnetic field, thicknesses, and thermal conductivity of the heart tissue. It was found that the frequency most affecting the tissue was 900 MHz. Our study found that the temperature distribution decreases with an increase in tissue thickness. We note that the results are similar for the different frequencies. It turns out that as the thermal conductivity of the heart tissue increases, the temperature distribution decreases slightly, while no change occurs here in the relationship between thermal conductivity and the temperature distribution of tissue with the change in the frequency of a wave.

Treatment Modelling of a 3D Tumour in Brain by Laser-Induced Interstitial Thermotherapy.

Introduction: There are some ways to examine heat transfer in tumor tissue, which is an important issue in bioengineering. One of these ways uses the bioheat equation, proposed by Pennes, in a continuous medium. Another one uses a porous medium to model heat transfer in living tissues. The objective of this paper was to study an approach to modelling the temperature distribution and tumour ablation in brain tissue and compare results to Pennes' approach. Methods: This approach presents and uses a porous medium as the tissue instead of a continuous medium. In addition, the two approaches (simulation in continuous and porous medium) are compared in terms of temperature simulation and amount of cell ablation. The density, heat conduction factor, and blood perfusion rate are considered functions of temperature. Results: In these approaches, after an 85-second treatment, the temperature increases to about 90°C. The temperature increase of the porous medium is relatively the same as that of the continuous medium and for this reason, the amount of cancerous cells that are ablated in a porous medium is approximately the same as that in a continuous medium. The volume of cell ablation is about 6500 mm3 for two ideas. In addition, the degree of damage, computed from the Arrhenius integral method, and the ablated volume of the tumour endorse equality at the end of treatment. According to the results, similar to the continuous approach, the porous approach predicts the temperature and amount of volume of damaged cells. Conclusion: Therefore, it is possible to use the porous approach instead of the Pennes approach for tumour treatment.

Thermal modulation of skin friction at the finger pad

Preliminary human studies show that reduced skin temperature minimises the risk of mechanically induced skin damage. However, the mechanisms by which cooling enhances skin tolerance to pressure and shear remain poorly understood. We hypothesized that skin cooling below thermo-neutral conditions will decrease kinetic friction at the skin-material interface. To test our hypothesis, we measured the friction coefficient of a thermally pre-conditioned index finger pad sliding at a normal load (5N) across a plate maintained at three different temperatures (38, 24, and 16 °C) in 8 healthy young adults (29±5y). To quantify the temperature distribution of the skin tissue, we used 3D surface scanning and Optical Coherence Tomography to develop an anatomically representative thermal model of the finger. Our group-level data indicated that the sliding finger with thermally affected tissues (up to 8 mm depth) experienced significantly lower frictional forces (p<0.01) at plate temperatures of 16 °C (i.e. 32% decrease) and 24 °C (i.e. 13% decrease) than at 38 °C, respectively. This phenomenon occurred consistently across participants (i.e. N = 6/8, 75%) and without large changes in skin hydration during sliding. Our complementary experimental and theoretical results provide new insights into thermal modulation of skin friction that can be employed for developing thermal technologies to maintain skin integrity under mechanical loading and shearing.

Laser Effects on Bioheat Transfer with Non-Newtonian Hybird Nanofluid Flow: Analytical Method with Finite Sine and Laplace Transforms

In this paper the effects of laser irradiation on MHD Non-Newtonian hybird nanofluid flow and bioheat transfer have been proposed. If the tissue is vertical and there is a sudden change in environmental temperature, free convection will flow and bioheat transfer must be solved in conjunction with hydrodynamics equations of nanofluid (blood) motion. The bioheat transfer within the tissue can be formulated in mathematical model as an initial and boundary value problem. The non-linear system of partial differential equations is solved analytically by applying Laplace transform with the help of finite Fourier sine transform. The energy equation assumes that the tissue temperature and blood phase are identical. The blood velocity profile is decreasing in parallel with the rise of fluid parameters. This implies that the medication conveyance therapy lessens the tumor volume and helps in annihilating malignancy cells by applying small parameters such as Casson parameter. The bioheat tissue temperature distribution increases as the both magnetite nanoparticles and multi-walled carbon nanotubes increase. Therefore, we enhance the physical properties of the blood by immersing the magnetite nanoparticles through it. The hybrid volume of nanoparticles will be more effective in enhancing blood velocity and tissue temperature by laser nanoparticle method.