Maxwell's equations-based dynamic laser–tissue interaction model

Ahmed, Elharith M.; Barrera, Frederick J.; Early, E A.; Denton, Michael L.; Clark, Clifton D.; Sardar, Dhiraj K.

doi:10.1016/j.compbiomed.2013.09.005

Cited by 9 publications

(7 citation statements)

References 35 publications

(34 reference statements)

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“…Assuming a constant peak temperature throughout the exposure duration [Eq. (5)], obtaining a conservative estimate of the Arrhenius A∕E a parameters for modeling damage relies on peak temperature values over a range of laser exposure durations. The similar threshold peak temperature values within exposure duration groups (Table 1) indicated that ambient temperature should not affect the values for Arrhenius A∕E a pairs.…”

Section: Arrhenius Modelmentioning

confidence: 99%

“…This could be important for modeling efforts to predict photothermal damage using Eq. (5). To quantify the significance of these variances, we calculated the factor by which each time point in the thermal profiles needed to be scaled in order to bring the damage integral values at τ to a value of 1 [Eq.…”

Section: Arrhenius Modelmentioning

confidence: 99%

“…Among these, computational models have gained acceptance in the laser safety community by filling data gaps across laser parameters with predicted laser dose responses. [4][5][6] Accurate damage projections can also conserve both time and cost by providing rational laser power ranges for use in subsequent animal studies. As an adjunct role to computational models, cell culture models can provide rapid damage response trends relative to varying laser parameters.…”

Section: Introductionmentioning

confidence: 99%

“…Equation (8) defines T crit and is calculated by taking the ratio of the slope to the y-intercept for data plotted per Eq. (5). Alternatively, the critical temperature can be calculated from any set of A∕E a pairs reported in the literature.…”

Section: Introductionmentioning

confidence: 99%

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Effect of ambient temperature and intracellular pigmentation on photothermal damage rate kinetics

et al. 2019

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Computational models predicting cell damage responses to transient temperature rises generated by exposure to lasers have implemented the damage integral (Ω), which time integrates the chemical reaction rate constant described by Arrhenius. However, few published reports of empirical temperature histories (thermal profiles) correlated with damage outcomes at the cellular level are available to validate the breadth of applicability of the damage integral. In our study, an analysis of photothermal damage rate processes in cultured retinal pigment epithelium cells indicated good agreement between temperature rise, exposure duration (τ), and threshold cellular damage. Full-frame thermograms recorded at high magnification during laser exposures were overlaid with fluorescence damage images taken 1 h postexposure. From the image overlays, pixels of the thermogram correlated with the boundary of cell death were used to extract threshold thermal profiles. Assessing photothermal responses at these boundaries standardized all data points, irrespective of laser irradiance, damage size, or optical and thermal properties of the cells. These results support the hypothesis that data from boundaries of cell death were equivalent to a minimum visible lesion, where the damage integral approached unity (Ω ¼ 1) at the end of the exposure duration. Empirically resolved Arrhenius coefficients for use in the damage integral determined from exposures at wavelengths of 2 μm and 532 nm and durations of 0.05-20 s were consistent with literature values. Varying ambient temperature (T amb) between 20°C and 40°C during laser exposure did not change the τ-dependent threshold peak temperature (T p). We also show that, although threshold laser irradiance varied due to pigmentation differences, threshold temperatures were irradiance independent.

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Section: Arrhenius Modelmentioning

confidence: 99%

Section: Arrhenius Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effect of ambient temperature and intracellular pigmentation on photothermal damage rate kinetics

et al. 2019

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“…Solutions to this inhomogeneous differential equation depend on the specific form of the input, as well as on the initial and the boundary conditions [12]. Solutions are commonly evaluated by means of numerical methods, as in [13][14][15][16]. These studies present models able to predict the temperature of tissue under specific conditions: laser wavelength, tissue type, temperature range.…”

Section: Monitoring Of Tissue Overheatingmentioning

confidence: 99%

Cognitive Supervision for Transoral Laser Microsurgery

Fichera

2016

Springer Theses

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The thermal laser-tissue interactions described in Chap. 2 constitute the basic building block of laser microsurgery. In particular, ablation by vaporization is the process by means of which laser incisions and resections are performed. As we shall see in this chapter, these interactions need to be carefully monitored to ensure the formation of incisions as planned by the surgeon. Control of the laser effects on tissue is essential for the purpose of ensuring a safe and efficient surgical performance, which encompasses both resection accuracy and limited thermal damage to underlying and surrounding tissues.Despite its importance, the control of the laser effects on tissues during Transoral Laser Microsurgery (TLM) is still performed manually, since available technologies do not include any support to monitor the state of tissues under laser irradiation. When analyzing the workflow of these interventions, it becomes apparent that the quality of laser resections depends entirely on clinicians, who must possess the experience required to anticipate and understand the results of their laser actions.This chapter introduces the problem of the automatic supervision of laser-induced effects during laser surgery. A top-down approach is used to tackle this problem: specific circumstances in which surgeons would value enhanced information regarding the effects of their laser actions on tissues are identified. The problem is grounded in the identification of variables of interest that are selected as target for the supervision. In the scope of this thesis, we explore the application of artificial cognitive approaches to monitor these variables in a surgical scenario.The first two sections of this chapter describe the current workflow of TLM and discuss its limitations from a clinical perspective. Our analysis is based both on evidence reported in medical literature, as well as on the opinions of clinicians who were consulted in the course of this doctoral research. In Sect. 3.3, we formulate the concept of a system capable of assisting clinicians during TLM, enhancing their perception of laser-induced effects on tissues. Two specific effects: thermal (temperature of tissues) and mechanical (laser cutting depth). We will review existing approaches to monitor these effects during laser irradiation, focusing on considerations regarding their

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Biological Tissues Axial Anisotropy Spatial Photometry

Bezugla

2023

Studies in Systems, Decision and Control

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Maxwell's equations-based dynamic laser–tissue interaction model

Cited by 9 publications

References 35 publications

Effect of ambient temperature and intracellular pigmentation on photothermal damage rate kinetics

Effect of ambient temperature and intracellular pigmentation on photothermal damage rate kinetics

Cognitive Supervision for Transoral Laser Microsurgery

Biological Tissues Axial Anisotropy Spatial Photometry

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