The use of optical fluence rate distribution for the differentiation of biological tissues

Hamdy, Omnia; El-Azab, Jala; Solouma, Nahed H.; Fathy, Mahmoud; Al-Saeed, Tarek A.

doi:10.1109/cibec.2016.7836129

Cited by 9 publications

(4 citation statements)

References 6 publications

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“…The optical properties of the skull have been calculated using the Kubelka-Munk mathematical method [ 25 ]. It is a transport model that estimates the absorption and reduced scattering coefficients of the examined tissue using the experimentally measured diffuse reflectance and transmittance [ 9 , 26 ]. In the present study, the diffuse reflectance R d and transmittance T d of the selected samples have been measured using a single integrating sphere-based optical setup.…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, some numerical methods such as Monte-Carlo (MCML) and inverse adding doubling (IAD) can be used for the same purpose [7]. However, utilizing most of the previously mentioned mathematical methods for determining tissue's absorption and scattering characteristics requires experimental data of the tissue's optical reflectance and/or transmittance [8,9]. These experimental data can be obtained by using either integrating spheres [10][11][12] or distant detectors [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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Variations in tissue optical parameters with the incident power of an infrared laser

Hamdy

Mohammed

2022

PLoS ONE

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Infrared (IR) lasers are extensively utilized as an effective tool in many medical practices. Nevertheless, light penetration into the inspected tissue, which is highly affected by tissue optical properties, is a crucial factor for successful optical procedures. Although the optical properties are highly wavelength-dependent, they can be affected by the power of the incident laser. The present study demonstrates a considerable change in the scattering and absorption coefficients as a result of varying the incident laser power probing into biological samples at a constant laser wavelength (808 nm). The optical parameters were investigated using an integrating sphere and Kubelka-Munk model. Additionally, fluence distribution at the sample’s surface was modeled using COMSOL-multiphysics software. The experimental results were validated using Receiver Operating Characteristic (ROC) curves and Monte-Carlo simulation. The results showed that tissue scattering coefficient decreases as the incident laser power increases while the absorption coefficient experienced a slight change. Moreover, the penetration depth increases with the optical parameters. The reduction in the scattering coefficients leads to wider and more diffusive fluence rate distribution at the tissue surface. The simulation results showed a good agreement with the experimental data and revealed that tissue anisotropy may be responsible for this scattering reduction. The present findings could be considered in order for the specialists to accurately specify the laser optical dose in various biomedical applications.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Variations in tissue optical parameters with the incident power of an infrared laser

Hamdy

Mohammed

2022

PLoS ONE

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“…Tissue diffuse reflectance and transmittance are the main measurements required to determine the optical parameters of any type of biological tissue, they can be experimentally measured by either integrating spheres [20] or distant photo-detectors [21]. Based on the reflection and transmission collected data, tissue absorption coefficient µa, scattering coefficient µs and anisotropy factor g can be estimated using various analytical approaches referred as indirect or inverse methods such as Kubelka-Munk mathematical model [22], inverse Monte-Carlo [23] and inverse adding-doubling iterative method [24].…”

Section: Introductionmentioning

confidence: 99%

Identifying different types of tumors in human brain, breast, and uterus according to their optical properties in the red to near-infrared region

Hamdy

2020

Preprint

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26Optical diagnosis techniques are gaining validity due to their competitive advantages 27 including safety and functionality. These methods probe the tissue using light in the red 28 to near-infrared region that shows relatively high penetration in biological tissues. The 29 reflected light which is controlled by tissue absorption and scattering parameters can 30 provide significant information about its pathology. In the present work, a method for 31 identifying different types of tumors in human brain, breast and uterus based on their 32 absorption and scattering characteristics in the red to near-infrared spectra is proposed. 33The classification method depends on studying the red to near-infrared light diffusion 34 through the examined tissues using Monte-Carlo simulation and finite element solution of 35 the light diffusion equation. The obtained tissue reflectance profiles, optical fluence rate 36 distribution and absorbed faction in each tumor type show different characteristics along 37 the selected wavelengths. The variation in these three features can be considered as an 38 alternative approach of medical diagnosis or can assist with other conventional diagnosis 39 methods. 40 41 42 Brain, breast and uterus are very vital and complex human body organs that have affected 43 by many types of tumors with different characteristics and symptoms. Diagnosing brain 44 tumors can be accomplished using different medical imaging techniques such as 45 magnetic resonance imaging (MRI) and computer tomography (CT) scan or by a 46 neurological exam [1]. While, X-ray mammography, MRI, breast ultrasonography and 48 CT can be used also to diagnose uterus tumors, transvaginal ultrasound and biopsy are 49 the most common utilized techniques [3]. However, many of these techniques have their 50 limitations. Besides, the use of ionizing radiation in the diagnosing process can even 51 develop tumors in the healthy tissues [4]. On the other hand, optical techniques are 52 considered very safe and functional diagnosing tools.53

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“…While, the experimental measurements is introduced to the inverse models in order to predict the unknown optical parameters [4]. Monte-Carlo simulation [5], diffusion equation [6] and adding doubling [3,7] method are examples of the forward models. While, Kubelka-Munk [8], inverse Monte-Carlo [9] and invers adding doubling [10] are inverse models.…”

Section: Introductionmentioning

confidence: 99%

Simulating Light Diffusion in Human Brain Tissues Using Monte-Carlo Simulation and Diffusion Equation

Hamdy¹

2018

AAS

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Medical diagnosis with optical techniques is favorable due to its safe and painless features. Every tissue type can be distinguished by its optical absorption and scattering properties that are related to many physiological changes and considered to be very important signs for tissue heath. Characterizing light propagation in the human brain tissues is a vital issue in many diagnostic and therapeutic applications. In this work, light propagation in different brain tissues in normal and coagulated state was investigated. A Monte-Carlo simulation model was implemented to obtain spatially resolved steady state diffuse reflectance profiles of the examined tissues. Furthermore, the diffusion equation was solved to create images presenting the optical fluence rate distribution at the tissue surface using the finite element method. The proposed diffuse reflectance curves and fluence rate images show different features regarding tissue type and condition that promises to be effective in medical diagnosis.

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The use of optical fluence rate distribution for the differentiation of biological tissues

Cited by 9 publications

References 6 publications

Variations in tissue optical parameters with the incident power of an infrared laser

Variations in tissue optical parameters with the incident power of an infrared laser

Identifying different types of tumors in human brain, breast, and uterus according to their optical properties in the red to near-infrared region

Simulating Light Diffusion in Human Brain Tissues Using Monte-Carlo Simulation and Diffusion Equation

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