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Charachterization of human skin by combined photothermal radiometry and diffuse reflectance spectroscopy
Verdel, Nina (Author), Majaron, Boris (Mentor) More about this mentor... This link opens in a new window, Lukač, Matjaž (Co-mentor)

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Abstract
In this thesis I combine two optical techniques to enable assessment of structure and composition of human skin in vivo: Pulsed photothermal radiometry (PPTR), which involves measurements of transient dynamics in mid-infrared emission from sample surface after exposure to a light pulse, and diffuse reflectance spectroscopy (DRS) in visible part of the spectrum. The analysis involves simultaneous fitting of measured PPTR signals and DRS spectra with corresponding predictions of a Monte Carlo model of light-tissue interaction. By using a four-layer optical model of skin (epidermis, papillary dermis, reticular dermis and subcutis) I obtain a good match between the experimental and model data when scattering properties of the epidermis and dermis are also optimized on an individual basis. The assessed parameter values correlate well with literature data and demonstrate the expected trends in controlled tests involving temporary obstruction of peripheral blood circulation using a pressure cuff, and acute as well as seasonal sun tanning. The obtained epidermal thickness values were tested by coregistration with a multiphoton microscope. Moreover, I evaluate the potential of this approach for quantitative evaluation of tattoos during laser removal treatment. For this purpose, I apply a three-layer optical model of skin consisting of epidermis, upper dermis, and bottom dermis which includes the tattoo ink. However, the involved iterative optimization of 14 skin model parameters using a numerical forward model (i.e., inverse Monte Carlo - IMC) is computationally very expensive. In order to overcome this drawback, we have constructed a very fast predictive model (PM) based on machine learning. The PM involves random forests, trained on $\sim$9,000 examples computed using my forward MC model.

Language:English
Keywords:sunkovna fototermalna radiometrija, difuzna refleksijska spektroskopija, karakterizacija kože, Monte Carlo, lasersko odstranjevanje tetovaž, napovedni model
Work type:Doctoral dissertation (mb31)
Tipology:2.08 - Doctoral Dissertation
Organization:FMF - Faculty of Mathematics and Physics
Year:2020
COBISS.SI-ID:31664387 This link opens in a new window
Views:141
Downloads:74
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Secondary language

Language:Slovenian
Title:Karakterizacija človeške kože s kombinacijo fototermalne radiometrije in difuzne refleksijske spektroskopije
Abstract:
Cilj tega doktorskega dela je določitev strukture in sestave človeške kože in vivo. V ta namen sem združila dve optični tehniki: sunkovno fototermalno radiometrijo (SFTR), ki temelji na zaznavanju infrardečega sevanja lasersko segretega vzorca in difuzno refleksijsko spektroskopijo (DRS) v vidnem delu spektra. Pri analizi sem sočasno prilagajala izmerjene PPTR signale in DRS spektre z ustreznimi napovedmi Monte Carlo modela interakcije med svetlobo in tkivom. Z uporabo štiri-plastnega modela kože (vrhnjica, zgornja in spodnja usnjica ter maščevje), pri katerem sem individualno prilagajala tudi sipalne lastnosti vrhnjice in usnjice, sem dobila dobro ujemanje med eksperimentalnimi in modelskimi podatki. Dobljene vrednosti parametrov človeške kože so zelo dobro primerljive z vrednostmi, ki jih najdemo v literaturi. Pri kontroliranih testih, kot so začasni zastoj periferne krvi z uporabo manšete za merjenje krvnega tlaka in akutna ter kronična izpostavljenost sončnemu sevanju, so vrednosti parametrov prav tako pokazale pričakovane trende. Vrednosti za debelino vrhnjice sem primerjala z vrednostmi dobljenimi z večfotonskim mikroskopom. Zgoraj opisan pristop sem uporabila tudi za kvantitativno karakterizacijo tetovaže na človeški kože med procesom laserskega odstranjevanja le-te. Za ta namen sem uporabila tri-plastni model kože sestavljen iz vrhnjice, zgornje usnjice in spodnje usnjice, ki vsebuje barvilo. Iterativna optimizacija 14 modelskih parametrov z uporabo numeričnega modela (i.e., inverzni Monte Carlo) je računsko zelo zahtevna. Da bi izboljšala to pomanjkljivost našega pristopa, smo zgradili zelo hitri napovedni model (NM), ki temelji na strojnem učenju. NM vsebuje naključne gozdove naučene na približno 9,000 primerih.

Keywords:pulsed photothermal radiometry, diffuse reflectance spectroscopy, skin characterization, Monte Carlo, laser tattoo removal, predictive model

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