In this work, photon fluencies have been used to explore the depth of buried layers for diffuse optical tomography (DOT) systems. Back-reflected diffuse reflectance data were generated by Monte Carlo ...Modelling of Light Transport (MCMLT) simulations. Heterogeneous layers which have 1mm thickness were buried from 1mm to 8mm for 8 different simulations, consecutively. DOT systems are categorized based on the physical appearence as transmission through or back reflection geometry. Transmission through systems are more successful than back-reflected geometrical models from the view of the accuracy of the locations of the reconstructed images. On the other hand in the real world, back-reflection geometry is necessary for diagnostics and treatment purposes in the medical units and hospitals. DOT imaging systems basically use photon transport diffusion equation inside the turbid media. In literature, researchers and companies are using the linearized model of non-linear diffusion equation which is the first Taylor term of Rytov or Born approximation of radiative transport equation (RTE). The unknowns of the linearized equation system are differences of absorption coefficients over homogeneous background. After modeling of the linearized equation system, the next step is to solve the linearized equation system. Finally, researchers and company workers are using the forward model in different mathematical inverse problem solution algorithms which can be grouped as iterative, subspace, and regularization methods. Regularization methods are used very frequently hence the measurement system usually has noises and artifacts and the forward model transfer functions matrix is also not square. In this work a new method is presented for DOT imaging modalities without using any mathematical inverse problem solution algorithms. This new philosophy is using comparison of photon fluencies for different depth layers between homogeneous and heterogeneous tissue models. The new philosophical approach is being presented to explore the depth of the buried layers for DOT systems.
An optical imaging model has been studied. Monte Carlo Modeling of Light Transport (MCMLT) has been run for the head model and photon fluencies have been generated. Multi-layered head model has seven ...different layers which are hair, epidermis, dermis, skull, cerebrospinal fluid, white matter, and gray matter with consecutive order from surface to deeper tissues. Back-reflected laser imaging geometry has been selected. Geometric multi source-detector positions are on the flat imaging surface. Source-detector matches are creating optode array on the imaging tissue surface. Each source-detector coupling is representing one single equation for diffuse optical tomography (DOT) system. In this work we had 64 sources and 64 detectors which makes 4096 equations. The equation system is building forward model. Forward model has independent equations which are equal to source-detector couplings. Our back-reflected continuous wave (CW) laser DOT model has been tested with simulation data by using the Tikhonov inverse problem solution algorithm. Forward model transport functions have been calculated. It has been built according to source-detector positions and grids. The Tikhonov inverse problem solution algorithm has been tested with different λ regularization parameters. The best parameter has been used for image reconstruction algorithm. One inclusion has been put into deeper voxels then image has been reconstructed. The result has been illustrated in xy, xz, and xyz coordinate grids with different angles. It has been reconstructed successfully with the correct position and concentration as well. This work gave an idea about how source-detector matches are important for back reflected DOT devices. As long as source-detector numbers are more than enough, reconstructed images are becoming more realistic as compared to the real inclusion location. This work encouraged us to design new laser tomography devices for near future. The algorithm has been tested with simulation data. Image reconstruction algorithms are important part of DOT devices. Progressive efforts are being made in academic research institutes and facilities. Algorithmic modeling is becoming very important and researchers are working to explore mathematical and philosophical ways to get better results. This work is presenting a new algorithmic approach to the laser imaging devices.
In this work, new image reconstruction schema has been proposed for back-reflected diffuse optical tomography geometry. 100 source and 100 detector points have been selected as bifurcated probe ...positions in xy plane. An x, y, and z cubic coordinate grid system has 10 × 10 × 30 mesh grids. In this work, a dynamic mesh grid concept has been introduced first. Each time source and detector positions are changed, x and y coordinate positions have been reassigned automatically. Centers between source and detector positions have been recalculated in xy plane. x and y grid positions have been reassigned around this center points. When using
predefined
static mesh grid photon fluencies which are coming from Monte Carlo (MC) simulation output or diffusion equation are transferred into static voxels which is only partially correct. Hence there is no symmetry around source–detector matchup center in xy plane and so is not totally correct. In this work, dynamic mesh grids have been created and photon fluencies have been assigned into automatically redefined dynamic mesh grid voxels. One dimensional depth profile has been used for simplicity. Median values of transport functions for each z depth grids have been used. Transport functions of photon fluencies from each source to detector positions have been calculated and each voxel value has been assigned in three dimensional dynamic mesh grid array. We innovated the use of dynamic mesh grid array instead of using static mesh grids which were being used previously, and now have assigned the photon fluencies into the dynamic mesh grid voxels. This also gave us an opportunity to use different transport functions for one dimensional image reconstruction schema. Before, we were using the sum of transport weight functions which are derived from each of the z depth layers. Now we have opportunity to use mean or median values also. Thus we are centering the dynamic mesh grid array by finding the center of each source and detector position successfully. This work gave us encouragement to build tomography devices.
