Abstract:Medical imaging techniques that are found in hospitals are X-ray imaging, MRI, ultrasound imaging, CT scan. Recently, a lot of scientific research work is being carried out with the aim of the improvement of these methods and the discovery of new non-invasive techniques. As a result, a new optical imaging technique, called Spectral Domain Optical Coherence Tomography (SD-OCT) was proposed in the early 2000s. Optical Coherence Tomography (OCT) is an emerging imaging technique having resolution in the μm range a… Show more
“…Firstly, we need to resample the wavelength array by interpolation so that the corresponding wavenumbers will be evenly spaced after conversion. An interpolating factor s i is calculated as where M is the length of the wavelength array and λ max = λ min + ( M − 1) δλ . Then a new array of wavelength is obtained as λ i = λ min + s i .…”
Optical coherence tomography (OCT) relies on optical interferometry to provide noninvasive imaging of living tissues. In addition to its 3D imaging capacity for medical diagnosis, its potential use for recovering optical parameters of biological tissues for biological and pathological analyses has also been explored by researchers, as pathological changes in tissue alter the microstructure of the tissue and therefore its optical properties. We aim to develop a new approach to OCT data analysis by estimating optical properties of tissues from OCT scans, which are invisible in the scans. This is an inverse problem. Solving an inverse problem involves a forward modeling step to simulate OCT scans of the tissues with hypothesized optical parameter values and an inverse step to estimate the real optical par1meters values by matching the simulated scans to real scans. In this paper, we present a Monte Carlo (MC)–based approach for simulating the frequency‐domain OCT. We incorporated a focusing Gaussian light beam rather than an infinitesimally thin light beam for accurate simulations. A new and more accurate photon detection scheme is also implemented. We compare our MC model to an analytical OCT model based on the extended Huygens‐Fresnel principle (EHF) to demonstrate the consistency between the two models. We show that the two models are in good agreement for tissues with high scattering and high anisotropy factors.
“…Firstly, we need to resample the wavelength array by interpolation so that the corresponding wavenumbers will be evenly spaced after conversion. An interpolating factor s i is calculated as where M is the length of the wavelength array and λ max = λ min + ( M − 1) δλ . Then a new array of wavelength is obtained as λ i = λ min + s i .…”
Optical coherence tomography (OCT) relies on optical interferometry to provide noninvasive imaging of living tissues. In addition to its 3D imaging capacity for medical diagnosis, its potential use for recovering optical parameters of biological tissues for biological and pathological analyses has also been explored by researchers, as pathological changes in tissue alter the microstructure of the tissue and therefore its optical properties. We aim to develop a new approach to OCT data analysis by estimating optical properties of tissues from OCT scans, which are invisible in the scans. This is an inverse problem. Solving an inverse problem involves a forward modeling step to simulate OCT scans of the tissues with hypothesized optical parameter values and an inverse step to estimate the real optical par1meters values by matching the simulated scans to real scans. In this paper, we present a Monte Carlo (MC)–based approach for simulating the frequency‐domain OCT. We incorporated a focusing Gaussian light beam rather than an infinitesimally thin light beam for accurate simulations. A new and more accurate photon detection scheme is also implemented. We compare our MC model to an analytical OCT model based on the extended Huygens‐Fresnel principle (EHF) to demonstrate the consistency between the two models. We show that the two models are in good agreement for tissues with high scattering and high anisotropy factors.
“…T 0 = 2(t max − t min ) Number of Peaks + Number of Valleys (2) Then the original irregular power envelope P(t) can be approximated by P cos (t) as shown in Eq. (3).…”
Section: Phenomenological Model For Spectral Broadening Of Incoherentmentioning
confidence: 99%
“…Subpulse period T 0 is defined as the lasting time of the generated incoherent light divided by the number of peak-valley pairs, which is written as Eq. (2).…”
Section: Phenomenological Model For Spectral Broadening Of Incoherent...mentioning
confidence: 99%
“…Incoherent lights sources, e.g. the superfluorescence source (SFS) and the superluminescent diode (SLD) source with no longitudinal modes but smoothly distributed photons within the spectral band is preferable for the applications of low coherence interferometry, optical coherence tomography (OCT), and optical gyroscopes [1][2][3]. Furthermore, the incoherent light may be a promising candidate for the inertial fusion driver, since it is helpful to reduce the Rayleigh Taylor (RT) hydrodynamic instabilities due to its smooth illumination in both time and space domain [4].…”
A phenomenological model for spectral broadening of incoherent light in silica fibers via self-phase modulation and dispersion is presented, aiming at providing a qualitative and readily accessible description of incoherent light spectral broadening. In this model, the incoherent light is approximated by a cosine power-modulated light with modulation parameters depending on the coherent time and the dispersion in fibers. A simple and practical method for spectral broadening predictions is given, and demonstrated by both the straightforward NLSE-based numerical modeling and series of experiments including narrowband and broadband incoherent light in passive fibers and fiber amplifiers.
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