Photonics simulation and modelling of skin for design of spectrocutometer

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An Introduction to Light Interaction with Human Skin

Despite the notable progress in physically-based rendering, there is still a long way to go before one can automatically generate predictable images of organic materials such as human skin. In this tutorial, the main physical and biological aspects involved in the processes of propagation and absorption of light by skin tissues are examined. These processes affect not only skin appearance, but also its health. For this reason, they have also been the object of study in biomedical research. The models of light interaction with human skin developed by the biomedical community are mainly aimed at the simulation of skin spectral properties which are used to determine the concentration and distribution of various substances. In computer graphics, the focus has been on the simulation of light scattering properties that affect skin appearance. Computer models used to simulate these spectral and scattering properties are described in this tutorial, and their strengths and limitations discussed. Keywords: natural phenomena, biologically and physically-based rendering

Multimodal optical spectroscopy for application in the life sciences

Many optical modalities are being investigated, applied, and further developed for non-invasive analysis and sensing in the life sciences. Often, the combination of two or more modalities is required for in depth analysis because of the complexity of the study objects and questions in this field. The work presents multimodal sensing concepts for applications ranging from non-invasive quantification of biomolecules in the living organism to supporting medical diagnosis showing the combined capabilities of Raman spectroscopy, optical coherence tomography, and optoacoustic

In vivo

Skin is a highly structured tissue, raising concerns as to whether skin pigmentation due to epidermal melanin may confound accurate measurements of underlying hemodynamics. Using both venous and arterial cuff occlusions as a means of inducing differential hemodynamic perturbations, we present analyses of spectra limited to the visible or near-infrared regime, in addition to a layered model approach. The influence of melanin, spanning Fitzpatrick skin types I to V, on underlying estimations of hemodynamics in skin as interpreted by these spectral regions are assessed. The layered model provides minimal cross-talk between melanin and hemodynamics and enables removal of problematic correlations between measured tissue oxygenation estimates and skin phototype

Appearance Modeling of Living Human Tissues

This is the peer reviewed version of the following article: Nunes, A.L.P., Maciel, A., Meyer, G.W., John, N.W., Baranoski, G.V.G., & Walter, M. (2019). Appearance Modeling of Living Human Tissues, Computer Graphics Forum, which has been published in final form at https://doi.org/10.1111/cgf.13604. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-ArchivingThe visual fidelity of realistic renderings in Computer Graphics depends fundamentally upon how we model the appearance of objects resulting from the interaction between light and matter reaching the eye. In this paper, we survey the research addressing appearance modeling of living human tissue. Among the many classes of natural materials already researched in Computer Graphics, living human tissues such as blood and skin have recently seen an increase in attention from graphics research. There is already an incipient but substantial body of literature on this topic, but we also lack a structured review as presented here. We introduce a classification for the approaches using the four types of human tissues as classifiers. We show a growing trend of solutions that use first principles from Physics and Biology as fundamental knowledge upon which the models are built. The organic quality of visual results provided by these Biophysical approaches is mainly determined by the optical properties of biophysical components interacting with light. Beyond just picture making, these models can be used in predictive simulations, with the potential for impact in many other areas

Improved mathematical and computational tools for modeling photon propagation in tissue

Thesis (Ph.D.)--Boston UniversityLight interacts with biological tissue through two predominant mechanisms: scattering and absorption, which are sensitive to the size and density of cellular organelles, and to biochemical composition (ex. hemoglobin), respectively. During the progression of disease, tissues undergo a predictable set of changes in cell morphology and vascularization, which directly affect their scattering and absorption properties. Hence, quantification of these optical property differences can be used to identify the physiological biomarkers of disease with interest often focused on cancer. Diffuse reflectance spectroscopy is a diagnostic tool, wherein broadband visible light is transmitted through a fiber optic probe into a turbid medium, and after propagating through the sample, a fraction of the light is collected at the surface as reflectance. The measured reflectance spectrum can be analyzed with appropriate mathematical models to extract the optical properties of the tissue, and from these, a set of physiological properties. A number of models have been developed for this purpose using a variety of approaches -- from diffusion theory, to computational simulations, and empirical observations. However, these models are generally limited to narrow ranges of tissue and probe geometries. In this thesis, reflectance models were developed for a much wider range of measurement parameters, and influences such as the scattering phase function and probe design were investigated rigorously for the first time. The results provide a comprehensive understanding of the factors that influence reflectance, with novel insights that, in some cases, challenge current assumptions in the field. An improved Monte Carlo simulation program, designed to run on a graphics processing unit (GPU), was built to simulate the data used in the development of the reflectance models. Rigorous error analysis was performed to identify how inaccuracies in modeling assumptions can be expected to affect the accuracy of extracted optical property values from experimentallyacquired reflectance spectra. From this analysis, probe geometries that offer the best robustness against error in estimation of physiological properties from tissue, are presented. Finally, several in vivo studies demonstrating the use of reflectance spectroscopy for both research and clinical applications are presented

Solar radiation and human health

The Sun has played a major role in the development of life on Earth. In Western culture, people are warned against Sun exposure because of its adverse effects: erythema, photoimmunosuppression, photoageing, photocarcinogenesis, cataracts and photokeratitis. However, Sun exposure is also beneficial, since moderate doses give beneficial physiological effects: vitamin D synthesis, reduction of blood pressure and mental health. Shortage of Sun exposure may be even more dangerous to human health than excessive exposure. Avoiding Sun exposure leads to vitamin D deficiency which is associated not only with rickets and osteomalacia, but also with increased risk of cardiovascular disease, multiple sclerosis, rheumatoid arthritis, diabetes, influenza, many types of cancer and adverse pregnancy outcomes. Solar radiation induces nitric oxide release in tissue and immediate pigment darkening which certainly play important roles, although these are still unknown. Action spectra relevant for health are described. We will also review what is known about spectral and intensity variations of terrestrial solar radiation as well as its penetration through the atmosphere and into human skin and tissue

Physics-based Computational Modeling of Human Skin using Machine Learning for Physiological Parameter Estimation

The skin in the largest organ in the human body and often subject to the greatest exposure to outside elements throughout one's lifetime. Current data by the World Health Organization suggests that more than 10 people die each hour worldwide due to skin related conditions. Many of these conditions include cancers, such as melanoma, which are growths that originate in the epidermis and if left untreated can spread throughout the body, reducing the chances of survival to less than 1%. If these tumors are detected during the early stages, the chances of survival are over 99%. Unfortunately, there only exist coarse diagnostic metrics, such as evaluations of color, texture, boundaries, and asymmetry, which are not sufficient for early detection of these cancers. In order to develop a screening technology, we require a non-invasive means of measuring the various biological components that make up the layers of the skin, i.e., melanosome concentration, collagen concentration and blood oxygen saturation, amongst others. The temporal analysis of changes in these components can serve as a critical tool in diagnosing the progression of these malignant cancers and in understanding the pathophysiology of cancerous tumors. Quantitative knowledge of these parameters can also be useful in applications such as wound assessment, drug delivery, and point-of-care diagnostics, amongst others. From a systems level perspective, we seek to develop a non-invasive, non-ionizing, and rapid technology that exposes an afflicted area on the skin to light, measures the amount of light that is reflected, transmitted, and/or absorbed, and using this information infers the concentration of each of the materials that make up the skin. Naturally, this inference would require a priori knowledge about the relationship between reflected light and concentration of biological materials. This is the goal of this thesis, the development of a computational model that relates the concentration of biological skin materials to a light reflectance measurement from the surface of the skin. This light reflectance measurement is obtained using hyperspectral imaging (HSI) or reflectance spectrometry. HSI allows for imaging well beyond the visible (VIS) region of the electromagnetic spectrum; past the near-infrared (NIR) and through the short wave infrared (SWIR). HSI allows us to obtain a reflectance measurement for each wavelength (band) spanning from 400 nm (VIS) to 1800 nm (SWIR). Imaging past the VIS can capture characteristic absorptions and other physiological makers typically exhibited by skin components outside the VIS region. In this thesis, we developed a method to estimate human skin parameters, such as melanosome concentration, collagen concentration, oxygen saturation, skin thickness, and blood volume, using hyperspectral radiometric measurements (signatures) obtained from in vivo skin. We developed a computational model based on Kubelka-Munk theory and the Fresnel equations. This model generates a forward mapping (a transformation) between skin parameters and a corresponding HSI reflectance spectra. This is a complex model, and not invertible. Therefore, we used machine learning based regression to generate the inverse mapping (the inverse transformation) between skin parameters and hyperspectral signatures. This yields a transformation (i.e., an inverse transformation) between the skin parameter vector space and the HSI signature vector space. Simply put, using a reflectance signature from a patch of skin, we can estimate the concentration of the biological materials that make up that patch of skin. Another challenge in the field has been that of obtaining ground truth. Methods to estimate skin parameters have been developed by several other studies, but no group has yet to compare their method to actual ground truth. Therefore, there is no direct way to assess the accuracy of the parameter estimation method. A major reason for this has to do with the practical difficulty associated with obtaining this ground truth; it involves biopsies and further biochemical analysis by a pathologist. For some parameters (e.g., melanosome concentration) it is unclear how one would proceed with determining the true concentration. For one skin parameter of dermatological interest, epidermal and dermal thickness, we developed a methodology based on Ultrasound imaging (US) to obtain a proxy ground truth against which to benchmark our machine learning method. For the first time, this provided a direct validation of the performance of the estimation methodology. We tested our methods using synthetic and in vivo skin signatures obtained in the VIS through the SWIR domains from 24 patients of both genders and Caucasian, Asian, and African American ethnicities acquired under IRB approval at the Johns Hopkins Hospital. Performance validation showed promising results: good agreement with the ground truth (average absolute error of 0.05+/-10e-3 percent) and well-established physiological precepts, as well as strong agreement with the gold standard obtained from Ultrasound imaging (mean error of 0.09+/-0.05 mm). Our early results suggested that our methods have potential use in the characterization of skin abnormalities and in non-invasive pre-screening of malignant skin cancers. Thesis Committee: Professor Philippe M. Burlina Professor Jeffrey H. Siewerdsen Professor Jon Meyerl

BioSpec: A Biophysically-Based Spectral Model of Light Interaction with Human Skin

Despite the notable progress in physically-based rendering, there is still a long way to go before we can automatically generate predictable images of biological materials. In this thesis, we address an open problem in this area, namely the spectral simulation of light interaction with human skin, and propose a novel biophysically-based model that accounts for all components of light propagation in skin tissues, namely surface reflectance, subsurface reflectance and transmittance, and the biological mechanisms of light absorption by pigments in these tissues. The model is controlled by biologically meaningful parameters, and its formulation, based on standard Monte Carlo techniques, enables its straightforward incorporation into realistic image synthesis frameworks. Besides its biophysicallybased nature, the key difference between the proposed model and the existing skin models is its comprehensiveness, i. e. , it computes both spectral (reflectance and transmittance) and scattering (bidirectional surface-scattering distribution function) quantities for skin specimens. In order to assess the predictability of our simulations, we evaluate their accuracy by comparing results from the model with actual skin measured data. We also present computer generated images to illustrate the flexibility of the proposed model with respect to variations in the biological input data, and its applicability not only in the predictive image synthesis of different skin tones, but also in the spectral simulation of medical conditions