arXiv subjects

Yoko Hoshi

Publications and source records attributed to Yoko Hoshi.

5 recordsLinked to original sources

arXiv2021

TRINITY: a three-dimensional time-dependent radiative transfer code for in-vivo near-infrared imaging

We develop a new three-dimensional time-dependent radiative transfer code, TRINITY (Time-dependent Radiative transfer In Near-Infrared TomographY), for in-vivo diffuse optical tomography (DOT). The simulation code is based on the design of long radiation rays connecting boundaries of a computational domain, which allows us to calculate light propagation with little numerical diffusion. We parallelize the code with Message Passing Interface (MPI) using the domain decomposition technique and confirm the high parallelization efficiency, so that simulations with a spatial resolution of $\sim 1$ millimeter can be performed in practical time. As a first application, we study the light propagation for a pulse collimated within $\theta \sim 15^\circ$ in a phantom, which is a uniform medium made of polyurethane mimicking biological tissue. We show that the pulse spreads in all forward directions over $\sim$ a few millimeters due to the multiple scattering process of photons. Our simulations successfully reproduce the time-resolved signals measured with eight detectors for the phantom. We also introduce the effects of reflection and refraction at the boundary of medium with a different refractive index and demonstrate the faster propagation of photons in an air hole that is an analogue for the respiratory tract.

physics.med-ph↗

arXiv2020

Deep Learning of Diffuse Optical Tomography based on Time-Domain Radiative Transfer Equation

Near infrared diffuse optical tomography (DOT) provides an imaging modality for the oxygenation of tissue. In this paper, we propose a novel machine learning algorithm based on time-domain radiative transfer equation. We use temporal profiles of absorption measure for a two-dimensional model of target tissue, which are calculated by solving time-domain radiative transfer equation. Applying a long-short-term memory (LSTM) deep learning method, we find that we can specify positions of cancer cells with high accuracy rates. We demonstrate that the present algorithm can also predict multiple or extended cancer cells.

physics.med-ph↗

arXiv2020

Decay behavior and optical parameter identification for spatial-frequency domain imaging by the radiative transport equation

The decay behavior of the specific intensity is studied for the spatial-frequency domain imaging (SFDI). It is shown using the radiative transport equation that the decay is given by a superposition of different decay modes, and the decay rates of these modes are determined by spatial frequencies and Case's eigenvalues. This explains why SFDI can focus on shallow regions. The fact that light with nonzero spatial frequency rapidly decays makes it possible to exclusively extract optical properties of the top layer of a layered medium. We determine optical properties of the top layer of a solid phantom. This measurement is verified with different layered media of numerical phantoms.

physics.optics↗

arXiv2019

A hybrid inversion scheme combining Markov chain Monte Carlo and iterative methods for determining optical properties of random media

Near-infrared spectroscopy (NIRS) including diffuse optical tomography is an imaging modality which makes use of diffuse light propagation in random media. When optical properties of a random medium is investigated from boundary measurements of reflected or transmitted light, iterative inversion schemes such as the Levenberg-Marquardt algorithm are known to fail when initial guesses are not close to the true value of the coefficient to be reconstructed. In this paper, we investigate how this weakness of iterative schemes is overcome by the use of Markov chain Monte Carlo. Using time-resolved measurements performed against a polyurethane-based phantom, we present a case that the Levenberg-Marquardt algorithm fails to work but the proposed hybrid method works well. Then with a toy model of diffuse optical tomography we illustrate that the evenberg-Marquardt method fails when it is trapped by a local minimum but the hybrid method can escape from local minima by using the Metropolis-Hastings Markov chain Monte Carlo algorithm until it reaches the valley of the global minimum. The proposed hybrid scheme can be applied to different inverse problems in NIRS which are solved iteratively. We find that for both numerical and phantom experiments optical properties such as the absorption and reduced scattering coefficients can be retrieved without being trapped by a local minimum when Monte Carlo simulation is run only about $100$ steps before switching to an iterative method. The hybrid method is compared with simulated annealing. Although the Metropolis-Hastings MCMC arrives at the steady state at about $10000$ Monte Carlo steps, in the hybrid method the Monte Carlo simulation can be stopped way before the burn-in time.

physics.comp-ph↗

arXiv2016

Numerical modeling of photon migration in human neck based on the radiative transport equation

Biomedical optical imaging has a possibility of a comprehensive diagnosis of thyroid cancer in conjunction with ultrasound imaging. For improvement of the optical imaging, this study develops a higher order scheme for solving the time-dependent radiative transport equation (RTE) by use of the finite-difference and discrete-ordinate methods. The accuracy and efficiency of the developed scheme are examined by comparison with the analytical solutions of the RTE in homogeneous media. Then, the developed scheme is applied to describing photon migration in the human neck model. The numerical simulations show complex behaviors of photon migration in the human neck model due to multiple diffusive reflection near the trachea.

physics.med-ph↗