Open access peer-reviewed chapter

# Diffuse Optical Tomography System in Soft Tissue Tumor Detection

Written By

Umamaheswari Kumarasamy, G.V. Shrichandran and A. Vedanth Srivatson

Submitted: May 24th, 2021Reviewed: June 3rd, 2021Published: June 30th, 2021

DOI: 10.5772/intechopen.98708

From the Edited Volume

## Digital Image Processing Applications

Edited by Paulo E. Ambrósio

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## Abstract

Topical review of recent trends in Modeling and Regularization methods of Diffuse Optical Tomography (DOT) system promotes the optimization of the forward and inverse modeling methods which provides a 3D cauterization at a faster rate of 40frames/second with the help of a laser torch as a hand-held device. Analytical, Numerical and Statistical methods are reviewed for forward and inverse models in an optical imaging modality. The advancement in computational methods is discussed for forward and inverse models along with Optimization techniques using Artificial Neural Networks (ANN), Genetic Algorithm (GA) and Artificial Neuro Fuzzy Inference System (ANFIS). The studies carried on optimization techniques offers better spatial resolution which improves quality and quantity of optical images used for morphological tissues comparable to breast and brain in Near Infrared (NIR) light. Forward problem is based on the location of sources and detectors solved statistically by Monte Carlo simulations. Inverse problem or closeness in optical image reconstruction is moderated by different regularization techniques to improve the spatial and temporal resolution. Compared to conventional methods the ANFIS structure of optimization for forward and inverse modeling provides early detection of Malignant and Benign tumor thus saves the patient from the mortality of the disease. The ANFIS technique integrated with hardware provides the dynamic 3D image acquisition with the help of NIR light at a rapid rate. Thereby the DOT system is used to continuously monitor the Oxy and Deoxyhemoglobin changes on the tissue oncology.

### Keywords

• Diffuse Optical Tomography (DOT)
• Near Infrared (NIR)
• Forward model
• Inverse model
• Regularization
• Artificial Neural Networks (ANN)
• Genetic Algorithm (GA) and Artificial Neuro Fuzzy Inference System (ANFIS)

## 1. Introduction

A recent survey was taken in the UK, reported 4,884 deaths from a brain tumor and about 11,633 deaths from breast cancer. The tumor detection is complicated and earlier detection leads to better chances of effective treatment, thereby increasing the survival rate. In the last decade, the concept of imaging has raised by the discovery of the X- ray radiography technique. The imaging techniques are highly meant for diagnostic applications in medical field. Different parts of a body have different range of absorption level hence the penetration of propagating light photon level varies for each and every organ, whereas this is the major concept considered for imaging. The imaging trend started with the X- ray radiography [1], it provides a one-dimensional image of the bony structures in a photographic film which could give the visualization of bony defects and the soft tissue tracks are identified only after the administration of contrast agents or dyes. The advanced version of the X- ray radiography is the Digital Radiography system which also provides a single plane image and it has the additional features such as data collection system, processing, display and storage system. Here the data obtained can be stored in a memory for future use. The limitation is even after the dye usage only the large variations in soft tissues can be identified.

In X - ray computed tomography the imaging of the organ is done in various angles and the reconstruction is demonstrated mathematically over the computer and displayed on the monitor. For the soft tissue examination, the dye fluids are pumped into the ventricles for providing the variation or contrast in the image. Here the noise increases inherently over the square root of the dose as the dose must be increased to preserve the same amount of noise. Therefore, over dosage leads to the side effects such as skin allergy, then came the existence the nuclear imaging.

Nuclear Medical Imaging (NMI) [1] systems utilize the radioisotopes for imaging. The small amount of radioactive chemicals is injected into the arm vein or inhaled through, and then the amount of radioactivity of the organ is examined using the radiation detectors. NMI includes Emission Computed Tomography which displays the single plane slice of the object with radioactivity, insisting same as.

X - ray computed tomography. In Single Positron Emission Tomography, gamma camera is used to create a three-dimensional representation of the radioisotope injected organ. Positron Emission Tomography (PET) imaging provides the cross- sectional images of positron emitting isotopes, which demonstrate the biological function and even physiological and pathological characteristics. The injected radioisotope may create allergic reactions and it takes hours to get clear from the blood and it’s a time-consuming process.

Magnetic Resonance Imaging (MRI) uses a magnetic field and high radio frequency signals to obtain anatomical information about the human body as cross- sectional images. The imaging technique needs the subject to be still while imaging, when there occurs a move and it blurs the output image. Radiations utilized here are highly ionized which causes harm and it is a tremendous time consuming and cost inefficient process for early tumor detection. The Ultrasonic imaging system is used for obtaining images of an almost entire range of internal organs in the abdomen. While it is completely reflected at boundaries with gas and there is a serious restriction in investigation of and through gas containing structures. The ultrasonic waves could not penetrate the bony structures hence imaging the brain is impossible.

Diffuse Optical Tomography (DOT) [2, 3] employs near infra-red light of range 700-1000 nm [4] which is non-invasive and non-ionizing radiation, therefore causes no harm or side effects. It has its main application of imaging the soft tissue organs such as the brain and breast for diagnosing tumor using the biological parameters [5, 6] such as oxygenation etc. The brain and breast tumor or lesion can be detected by examining the oxygenated, deoxygenated hemoglobin, water and lipids (proteins). DOT imaging [7] provides a number of advantages, such as reduced size setup in turn lead to portability, real-time imaging, low instrumental cost and less time consumption when compared to the other imaging techniques but is generally known to have a low image resolution which limits its further clinical application. Table 1: Compares Biomedical Imaging Modalities- Diffuse optical tomography evaluated with Computer Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron Emission Tomography (PET). The parameters namely cost, imaging time, size, sensitivity and specificity are compared.

ParametersDOTCTMRIPET
Cost$150,000$300,000$1,000,000$1,446,546
Imaging Time15–20 mins45–60 mins45–70 mins75–90 mins
Size60 x 45 cm50 x 50 cm4x4m25x36x17cm
Sensitivity50%90%91%93%
Specificity100%56%71%70%

### Table 1.

Comparison of biomedical imaging modalities.

The main absorbers of near-infrared (NIR) light in blood-perfused tissues are Oxy-hemoglobin, deoxyhemoglobin, Lipids (Bulk proteins) and water. NIR Spectral Window absorption spectra are between 650 and 1000 NM are shown in Figure 1 is obtained from compiled absorption data for water [8] and hemoglobin [9]. Hence, light in this spectral window penetrates deeply into tissues, thus allowing for non-invasive investigations. The NIR light penetration depth into tissues is limited, by the hemoglobin absorption at shorter wavelengths and by the water absorption at longer wavelengths.

Different systems in DOT are Continuous Wave (CW) imaging [6], Time Domain (TD) and Frequency Domain (FD). Continuous imaging is the study of hemodynamic and oxygenation changes in superficial tissues. It requires a source of constant intensity modulated at low frequency. Measuring the intensity of light transmitted between two points on the surface of the tissue is economical. Optimum sensitivity is achieved by a number of distinct sources and detectors. Intensity measurements are sensitive and are unable to distinguish between the absorption and scattering effects. Time Domain (TD) system uses photon counting detectors, slow but highly sensitive. The temporal distribution of photons is produced in short duration. Short pulses of light are transmitted through a highly scattering medium known as a Temporal Point Spread Function (TPSF). Frequency Domain (FD) [9] system is relatively inexpensive, easy to develop and provides fast temporal sampling up to 50HZ.The system acquires quick measurements regarding the amplitude and phase of scattering and absorption in the frequency domain at high detected intensities.

## 2. Methods

### 2.1 Forward model

The NIR light propagates within the biological tissue in a turbid medium [5]. Light particles scatters with cell particles and the medium either absorbs or scatter the light. The positions and orientations of scatters are described by mesoscopic and macroscopic. In mesoscopic the particles in turbid media of dense concentration and light transport are modeled by Radiative Transport Equation (RTE) [3]. In macroscopic photon transport on mean free path, diffusion approximation holds good for turbid media. Therefore, the isotropic scattering effect and light transport within the tissues is described by the diffusion Equation.

Light transport in tissues derived using RTE, assumes the energy particles do not change in collisions hence refractive index is constant with the medium [8]. RTE is used to describe anisotropic field and the photon propagation in tissue, is given by

ŝ.Irωŝ+μa+μs+cIrωŝ=μsfŝŝIrωŝd2ŝ+qrωŝE1

I(r,ω, ŝ) is radiance with modulation frequency ωat point r, in the direction ŝ. μa, μs are absorption and scattering coefficients respectively and c is the speed of light. The scattering phase function.

f (ŝ,ŝ')is used to characterize the intensity of a beam, that is scattered from the direction ŝ'into the direction ŝ. The scattering phase function commonly used Henyey - Greenstein scattering function.

fcosθ=14π1g21+g22gcosθ3/2E2

where θ is the angle between the two directions ŝ and ŝ’, and g is the anisotropy factor which is used to characterize the angular distribution of tissue scattering.

The fluence at point r modulation frequency ω and in the direction ŝ is defined by

φrω=4πI(r,ω,ŝ)E3

The Monte Carlo Method is used to solve the radiative transfer Equation.

#### 2.1.2 Diffusion approximation

The directional flux magnitude is less compared to isotropic fluence magnitude within the tissue. The light field ‘diffuses’ means the scattering interaction dominates over absorption. The diffusion equation [10] approximation is given as

Krφrω+μar+crφrω=q0rωE4

μarabsorption coefficient, q0rωis isotropic source, Φ(r,ω)is photon influence rate with modulation frequency ωat position r. The velocity of light in medium c(r) at any point r is defined as c0/nrwhere c0 is the speed of light in vacuum, n(r) is the index of refraction.

Diffusion coefficient is described as

Kr=13μar+μsrE5

Where the reduced scattering coefficient is μs'r=μsr1gr, g(r) is anisotropy factor. The refractive index mismatch at the tissue boundary is eluded by applying Robin boundary condition (type III). Eq. (5) solved using Finite Element Method (FEM) which provides stable solution [11].

## 3. Modeling techniques

### 3.1 Analytical model

Analytical model has fast computation and the Green’s function is applied for modeling the diffusion equation or RTE analysis. The Green’s function provides a solution when the source is a spatial and temporal function. It is commonly used to solve the forward problem for image reconstruction, specifically for fast imaging techniques. Optical properties are modeled by a green’s function [3] for a slab representing the homogeneous background; with an additional perturbation term represent the spherical insertion.

### 3.2 Statistical model

Models the individual photon with Poisson error incorporated in the model. Monte Carlo method is a gold standard statistical technique in diffuse optics. The geometry of the model is defined by μa, μs, the refractive index and the photon trajectories. Light propagation in non- diffusive domains is calculated by Monte Carlo techniques. Random walk theory provides a distinct approach in which photon transport is modeled as a series of steps on the discrete cubic lattice. Random walk theory [9] is particularly suited to model time-domain measurements. The random walk extension technique has been developed for modeling media with anisotropic optical properties, maintaining the cubic lattice.

### 3.3 Numerical model

Numerical techniques have the potential for modeling complex geometries. Finite Element Method (FEM) [8] is used to represent the inhomogeneous distribution [12] of optical properties in an arbitrary geometry. Boundary Element Method (BEM) [3], Finite Difference Method (FDM) and Finite Volume Method (FVM) are applied in more specialized applications. Finite Element Method divides the reconstruction domain into finite element meshes. The optimal computational efficiency of FEM depends on the smallest number of elements to represent the internal field by a finite element mesh. Adaptively refine the mesh by placing more elements when the field changes rapidly.

## 4. Regularization

The ill-condition inverse problem [5] in image reconstruction provides poor localization of imaging in localized or sparse regions. To overcome the ill posed problem in inverse model, regularization is applied in inverse model. The various forms of regularization are standard/Tikhonov regularization, exponential/spatial regularization, generalized least mean square regularization, adaptive regularization and model-based regularization [8, 11, 13, 14, 15].

### 4.1 Standard regularization

Standard regularization [16] or constant regularization is of Tikhonov type. Here the regularization is based on the already available information that may be the noise characteristics [11] or structural information [17] of the data, more prior information [13] usage leads to a better outcome of reconstruction procedure or robustness to the noise in the data.

Pμa=λTμa2E6

λ is a regularization parameter (i.e.) constant chosen to stabilize the solution and its value varies from 1e-6 to 10.

λT=σy2/σμμ02E7

The ill posed problem with inverse model is solved by adding the penalty term to the objective function.

Ω=yGμa2+PμaE8

y = ln (A) is the measured experimental data here A specifies the amplitude, G (μa) modeled data and penalty term is P(μa) removes the high frequency components. Iteratively linearization minimizes “Ω” by ∂Ω∂μa=0.Using Taylor series of expansion Tikhonov minimization is obtained by

Ω=yGμa2+λTLμaμ02E9

L is the dimensionless matrix and μ0 is prior estimate. The penalty term minimization scheme along with linearization leads to the updated equation (Gauss-Newton update equation)

JTJ+λIμa=J(yGμaE10

‘J’ is a Jacobian ∂G (μa) / ∂ (μa) gives the rate of change with modeled data with respect to μa and I represent the Identity matrix. The diffuse optical tomography inverse problem sets a least square problem, which is solved by matching experimentally measured boundary data with modeled data iteratively.

Linearization of the as in (7) leads to an updated equation.

JTJλLTL=JTδi1λTLTLμi1μ0E11

The δi1represents data misfit model and T for transpose operation. The resolution provided by as in (9) concentrates more on the detected position.

In adaptive regularization [15] the regularization parameter λ varies with respect to the projection error [18]. Projection error Φ is defined as the difference in measured data in the modeled data which is expressed as in (12).

φ=yGμa2E12

The regularization parameter λ is denoted as

λ=1/2+eΔφE13

The regularization parameter λ varies in the range from 1/3 to 1/2. As in (13) ‘e’ represents the exponential function and ∆Φ representing the change in projection error. A penalty term for projection error-based regularization is expressed as

Pμa=λΔφμa2E14

Linearization leads to an updated equation.

Δμ=JTJJT+λJJTI1φE15

As in Eq. (15) ∆μ represents the change in absorption coefficient. Projection error determines the accuracy, while JJT is denoted as the Hessian matrix with diagonal elements.

### 4.3 Exponential regularization

Exponential regularization is otherwise called as spatially varying regularization [14] or wavelength chromophore specific regularization, which is based on the physics of the problem. This simplicity makes it widely used for solving inverse problems especially in the cases where the prior information is not available. λ(r) is spatially varying regularization parameter, where r represents the position spatially. The spatial variation is attained by an exponential function in the form

λr=λeexpr/RλcE16

As in (16) the radius of imaging domain is R, λc, λe are the regularization parameters at the edge and center of the location. The spatially varying regularization has exponential term with low value at the center and large value near the boundary of the imaging domain in-order to neutralize the hypersensitivity near the boundary, which appears due to detectors located at the boundary. In order to determine the regularization parameter λ(r), the generalized objective function is given as,

Ω=yGμ2+λrLμμ02E17

As in (17) L is a dimensionless regularization matrix and μ0 is the prior estimate the of properties, while the penalty term for exponential regularization is represented as

Pμa=λrμa2E18

Linearizing (17) leads to a Jacobian updated equation as

JTJλrLTL=JTδi1λrLTLμi1μ0E19

Exponential Regularization captures the hessian matrix diagonal as JTJ.

### 4.4 Model based regularization

Model based regularization utilizes the combination of model resolution matrix [19] and data resolution matrix. The objective is to match the modeled data with the observed data. By this method of regularization, the spatial resolution of the reconstructed image is improved [18].

y=GμaE20

Expanding using Taylor series gives the equation

y=Gμa=Gμa0+Gμaμaμa0+μaμa0TG"μaμaμa0+..E21

Jacobian matrix J = G′ (μa) and Hessian matrix H = G″ (μa).

Linearizing (21) then

y=Gμa0+Jμaμa0E22

using y - G (μa0) = δ and μa = μa- μa0 [20].

Updated equationδ=μaE23

Change in absorption coefficient (Δμa) is derived as in (18) as

Δμa=JTJ+λI1JTμaE24

In the case of λ = 0

Δμa=ΔμaE25

Regularization term is linearized using the model resolution matrix, which depends on the forward model and regularization but not on data. Because of the ill posed nature of the problem as in (25) λ > 0, then

ΔμaΔμaE26

As in Eq. (24) leads to a model resolution matrix.

M=JTJ+λI1JTJE27

As in (27) M has the dimension of NN x NN and it purely depends on JTJ and the regularization term used. Linearization of as in (27) leads to an updated Jacobian matrix. λ varies from 0 to 1.

JTJ+cλiIΔμa=JTyGμaE28

The regularization parameter of a model resolution matrix is given as

λim=Mii/maxMiifori=1,2,NNE29

The model resolution matrix can be applied for deriving the linearization as in (26) for both constant and spatially varying regularization parameters. The matrix varies for constant and spatially varying regularization. The model resolution matrix main aim is to provide the better resolution characteristics without depending on data.

The data resolution matrix concentrates only on the data not on the image characteristics [21]. It defines that how well the estimated Δμa fits the observed data, hence it is important to consider data too in order to improve the resolution characteristics.

μa=δE30

Data resolution matrix is derived using the Jacobian matrix (J) and the regularization technique which is used for reconstruction. It is evaluated by matching the predicted data with the obtained data [22].

δ=yGμa0E31

The data-resolution matrix does not depend on a specific data (y) or error in it but are exclusively the properties of J and the regularization (λ) used. The closer it is to the identity matrix, the smaller are the prediction errors for δ, where δ` as in (31) representing the data misfit.

Data resolution matrix D is given as

D=JTJJJT+λI1E32

Linearizing (31) leads to an updated equation

Δμi=JTJJT+λI1δi1E33

The regularization parameter of a data resolution matrix is given as

λid=Dii/maxDiifori=1,2,NNE34

As in (26) and as in (34) the regularization parameter of the model-based regularization λi is given as

λi=λim+λid/2E35

Penalty term for the regularization scheme is given as

Pμa=cλiμa2E36

Where c provides the weight for penalty term and it is a constant term.

## 5. Inverse model

Newton - Raphson iterative method to find the optical parameter μa, μs by solving the minimum objective function.

ψμ=φmφc2+λμμ02E37

Φm and Φc are calculated and measured radiance at the detectors. λ is regularization parameter, μ, μ0 are current and initial estimates of optical properties at each node.

The initial values of absorption and scattering properties were estimated homogenously [23]. Update Φoptical distribution in Tikhonov Regularization is given by Eq. (39).

μ=JTJJT+λHmaxI1φmφcE38

∆μ Optical parameter update vector, Hmax maximum main diagonal element value of the matrix JJT. J is Jacobian matrix for inverse problem plots the variation in log amplitude and phase for both absorption and diffusion modification in every node.

### 5.1 Jacobian reduction

Jacobian matrix J has the size as number of measurements NM by the number of FEM nodes NN i.e. NM x NN is calculated using ad joint method. Limit the Jacobian [23, 24] to the measured amplitude data and optical absorption. Jacobian links a change in log amplitude, at the boundary with a change in absorption coefficient μa.

J=lnI1μa1lnI1μaNNlnINMμa1lnI1μaNNE39

The size of the Jacobian matrix is reduced by calculating the total sensitivity throughout the imaging domain and a new Jacobian Jij˜is formed [25].

J˜ij=Jijifi=1NMJijthreshold0ifi=1NMJij<thresholdE40

‘j’ corresponds to a node number within the domain. Reduction of Jacobian matrix improves the computational speed and efficiency of image reconstruction.

### 5.2 Bayesian framework

Ill posed condition of DOT problem, the solution is robust. To overcome this problem a priori information is incorporated constraint in the space of unknowns. Bayesian approach proposes an algorithm for spatial physiological prior [26]. High resolution anatomical image is segmented into sub-images. Each image is assigned a mean value with a prior probability density function of the image. ‘Confidence level’ is defined in the form of an image variance formulation to allow local variations within sub-images. MAP (Maximum a posteriori) estimates of the image [26, 27] is formed based on the formulation of the image’s probability density function.

ŷMAP=argmaxlogpy/x+logpxE41

p(y/x) is log likelihood function; p(x) is a probability density function.

Alternating minimization algorithm sequentially updates the unknown parameters, solves the optimization problem. Probability density function of the ith sub-image is defined in the spatial prior as

pxi/σi=12σi2Ni/2exp12σi2XiCi2,i=1,2.ME42

M is number of sub regions, Ni is number of voxels in the ith sub image, xi is the unknown sub image, Ci is chromosphere mean concentration, σi2is single variance.

The confidence level is incorporated into the statistical reconstruction procedure, the sub-image variance.

pσi=12γi2Ni/2exp12γi2σiσj2,i=1,2,ME43

γiis the variance and σiis the mean value of σi.

## 6. Experimental set-up

The practical setup of image acquisition as shown in Figure 2 includes optical components, electrical components, control, data acquisition and image reconstruction [28].

The optical Multiplexer has three parts namely the motor, drive and Black box (PMT) Photon multiplier tube. Driver rotates the optical multiplexer to guide the energy to PMT, which converts light to electrical signals. The signal is amplified by an Amplifier and preprocessed electrical signals are given to Data acquisition card. Data acquisition software samples the raw data, post process and controls the hardware. The personal computer delivers commands to alter the fiber switch (source channel) sequentially. 16 X 16 input and output fibers constitutes to 256 sources – detector pairs. Image is reconstructed using inverse modeling such as Jacobian reduction with FEM.

The optical Multiplexer has three parts namely the motor, drive and Black box (PMT) Photon multiplier tube. Driver rotates the optical multiplexer to guide the energy to PMT, which converts light to electrical signals. The signal is amplified by an Amplifier and preprocessed electrical signals are given to Data acquisition card. Data acquisition software samples the raw data, post process and controls the hardware. The personal computer delivers commands to alter the fiber switch (source channel) sequentially. 16 X 16 input and output fibers constitutes to 256 sources – detector pairs. Image is reconstructed using inverse modeling such as Jacobian reduction with FEM.

## 7. Optimization techniques

### 7.1 Artificial neural networks

Artificial Neural Network (ANN) is data structure accurately approximates a nonlinear relationship between a set of input and output parameters. It maps the input optical properties for spatial frequency domain in inverse modeling. Perform Monte Carlo simulation and fit it to ANN to output the data. Neural Network is trained to predict the tissue reflectance for strongly and weakly absorbing media.

The parallel Back propagation neural network distinguishes nonlinear relationship between spatial location of tumors and light intensity around the boundary of the tissue [29]. The neural network is trained for fast reconstruction in diffuse optical tomography. Location and spacing of optical sources and detectors are optimized using neural network. To improve the resolution of DOT images in inverse model Fixed Grid Wavelet Network [30] image segmentation is applied to extract a smooth boundary in tumor images.

### 7.2 Genetic algorithm

Reconstruction of optical in homogeneities embedded in turbid medium using diffuse optical tomography. The optimization problem is solved by using genetic algorithm minimizing objective function [31, 32]. This approach is applied for full non- linear range of quantitative reconstruction. Crosstalk near the source detector artifacts are the major inaccuracies in diffuse optical tomography images [33]. This problem can be solved by a global optimization method namely genetic algorithm for estimating the optical parameters.

### 7.3 Adaptive neuro fuzzy interference system

Adaptive Neuro fuzzy Inference system can be used for optical imaging, solving the non-linear ill posed problem with accurate qualitative and quantitative optical image reconstruction. The proposed method using ANFIS architecture will provide fast and accurate optical image reconstruction hence can achieve classification accuracy, volume and the layer thickness measurement of tumor.

## 8. Simulation techniques

The simulation software for modeling diffuse optical tomography is CULA, NIRFAST NETGEN and MIMICS. CULA is GPU Accelerated Linear Algebra which has a parallel computing architecture to dramatically improve the computation speed of sophisticated mathematics and also contains routines for systems solvers, singular value decompositions and Eigen problems. For reconstruction in diffuse optical tomography it facilitates singular value decomposition, matrix multiplication, matrix inversion etc.

NIRFAST is Near Infrared Fluorescence and Spectroscopy Tomography [33, 34] which is an FEM based software package designed for modeling Near Infrared Frequency domain [35] light transport in tissue.

NETGEN [36] is an automatic 3D tetrahedral mesh generator which accepts input from Constructive Solid Geometry (CSG) or Boundary Representation (BR) from the STL (Stereo Lithography) file format. It contains modules for mesh optimization and hierarchical mesh refinement and it is also open-source software available for Unix/Linux and Windows.

MIMICS is software specially developed for medical image processing. The ROI (Region of Interest) is selected in the segmentation process which is converted to a 3D surface model using an adapted marching cubes algorithm that takes the partial volume effect into account, leading to very accurate 3D model. The 3D files are represented in the STL format.

## 9. Conclusion

The Diffuse Optical Tomography (DOT) imaging experimental setup has three kinds of noise namely thermal noise, shot noise and relatively intensity noise. The shot noise from dark current of photodetector has Poisson statistics, solved by using Bayesian network in inverse problem. DOT has undetermined problem due less measured data in the forward model compared to the pixels reconstructed in inverse model. The forward problem solved by FEM and regularization techniques to improve the spatial resolution of DOT images. Diffuse Optical Tomography (DOT) has significant advancement since it becomes faster, more robust, less susceptible to error, and able to acquire data at a number of wavelengths with more source–detector combinations. Images reconstructed in 3D, uses more sophisticated techniques, which can be adapted by incorporating prior information and by compensating for some of the unavoidable sources of measurement error. DOT imaging is still a laboratory-based technique, yet to progress to develop a handheld for detection of tumor in morphological tissues in clinical applications. Qualitative and quantitative accuracy has to be improved in DOT, both of which are limited by poor spatial resolution. Improved image quality is achievable by adopting the optimization techniques namely Artificial Neural Networks, Genetic Algorithm and Adaptive Neuro Fuzzy Inference System. Enhancement of DOT can also achieve higher performance using multimodal imaging techniques. DOT is as a low-cost, portable imaging system to be developed at the bedside. The best modeling and reconstruction methods provide an ideal DOT instrumentation.

## Acknowledgments

The research was carried out in Department of Electronics and Communication and Department of Mechanical Engineering in SRM TRP Engineering College, Trichy, Tamil Nadu, India. We express our gratitude to our management, faculty and research scholars in SRM TRP Engineering College, Trichy, Tamil Nadu, India, who provided skill and proficiency that completely helped in the development of an optimized DOT instrument for detection of sarcoma cells. We are very grateful to Radiation Oncology Centre at Trichy SRM Medical College Hospital & Research Centre, Tiruchirappalli for providing the insights on tumor detection.

## Conflicts of interest

Our project promotes the social awareness on early tumor detection at cellular level. Diffuse Optical Tomography (DOT) provides harmless non-invasive detection of tumor cells. Today around 70% of people are suffering from sarcoma in soft tissue. Detection at earlier stage helps the patient for early diagnosis and prevents them from clinical pathology. We are interested to promote our review based on diffuse optical tomography instrumentation without any conflicts of interest as review article.

## Compliance with ethical standards

1. Disclosure of potential conflicts of interests.

2. Conflict of Interest: The authors declare that they have no conflict of interest.

3. Research involving human participants and/ or animals.

4. Ethical approval: “This chapter does not contain any studies with human participants or animals performed by any of the authors.”

## References

1. 1.John G. Webster 2003: Medical Instrumentation Application and Design Wiley Third edition. DOI: 10.1097/00004669-197807000-00017
2. 2.S. L. Jacques and B. W. Pogue 2008: Tutorial on diffuse light transport J. Biomed. Opt., 13, 04130.https://doi.org/10.1117/1.2967535
3. 3.A. Gibson, J. C. Hebden, and S. R. Arridge 2005: Recent advances in diffuse optical tomography Phys. Med.Bio. 50, R1–R43.https://doi.org/10.1117/1.2967535
4. 4.S. R. Arridge 1999: Optical tomography in medical imaging Inverse Probl., 15, R41–R93.https://doi.org/10.1007/978-94-010-0975-1_17
5. 5.S. R. Arridge and J. C. Hebden 1997: Optical imaging in medicine: II. Modelling and reconstruction Phys.Med.Biol. 42, 841–853. DOI: 10.1088/0031-9155/42/5/008
6. 6.H. Dehghani, S. Srinivasan, B. W. Pogue, and A. Gibson 2009: Numerical modelling and image reconstruction in diffuse optical tomography Phil. Trans. R. Soc.,A 367, 3073–3093. doi: 10.1098/rsta.2009.0090
7. 7.A. Gibson and H. Dehghani 2009: Diffuse optical imaging Phil. Trans. R. Soc., A 367, 3055–3072. doi: 10.1098/rsta.2009.0080
8. 8.Hale, G. M., and M. R. Querry 1973: Optical Constants of Water in the 200 nm to 200 μm Wavelength Region, Appl. Opt. 12, 555-563.https://doi.org/10.1364/AO.12.000555
9. 9.http://omlc.ogi.edu/spectra/hemoglobin/summary.html.
10. 10.K.Uma Maheswari, S. Sathiyamoorthy, 2016: Soft tissue optical property extraction for carcinoma cell detection in diffuse optical tomography system under boundary element condition, OPTIK 127 (2016) 1281-1290.https://doi.org/10.1016/j.ijleo.2015.10.100
11. 11.Michael Chu, Karthik Vishwanath, A.D Klose and Hamid Dehghani 2009: Light transport in biological tissue using three - dimensional frequency-domain simplified spherical harmonics equations Phys Med Biol.2009 Apr 21; 54 (8): 2493-509. doi: 10.1088/0031-9155/54/8/016
12. 12.K. UmaMaheswari, S. Sathiyamoorthy, G. Lakshmi, 2016: Numerical solution for image reconstruction in diffuse optical tomography, journal of engineering and applied sciences Vol.11, No.2, pp. 1332-1336.www.arpnjournals.org/Jeas/research_papers/rp_2016/jeas_0116_3501.Pdf
13. 13.M. Schweiger, S. R. Arridge, M. Hiroaka, and D. T. Delpy 1995: The finite element model for the propagation of light in scattering media: Boundary and source conditions Med. Phys., 22, 1779–1792. doi: 10.1118/1.597634
14. 14.S. R. Arridge and M. Schweiger 1995: Photon- measurement density functions. Part 2: Finite-element- method calculations Appl. Opt., 34, 8026–8037.https://doi.org/10.1364/AO.34.008026
15. 15.M. Guven, B. Yazici, X. Intes, and B. Chance 2005: Diffuse optical tomography with a priori anatomical information Phys. Med. Biol., 50, 2837–2858.https://doi.org/10.1117/12.479799
16. 16.B. W. Pogue, T. McBride, J. Prewitt, U. L. Osterberg, and K. D. Paulsen 1999: Spatially variant regularization improves diffuse optical tomography Appl. Opt., 38, 2950–2961.https://doi.org/10.1364/AO.38.002950
17. 17.H. Niu, P. Guo, L. Ji, Q. Zhao, and T. Jiang 2008: Improving image quality of diffuse optical tomography with a projection-error based adaptive regularization method Opt. Express 16, 12423–12434.https://doi.org/10.1364/OE.16.012423
18. 18.P. K. Yalavarthy, B. W. Pogue, H. Dehghani, and K.D. Paulsen 2007: Weight-matrix structured regularization provides optimal generalized least-squares estimate in diffuse optical tomography Med. Phys., 34, 2085–2098. doi: 10.1118/1.2733803
19. 19.R. Sukanyadevi, K. Umamaheswari, S. Sathiyamoorthy, 2013: Resolution improvement in diffuse optical tomography, IJCA ICIIIOES(9),3741.https://www.ijcaonline.orgproceedings/iciiioes/number9/14347-1656
20. 20.F.Larusson, S.Fantini and E. L Miller 2011: Hyperspectral image reconstruction for diffuse optical tomography Biomed. Opt. Express, 2, 946–965. doi: 10.1364/BOE.2.000946
21. 21.Deepak Karkala and Phaneendra K. Yalavarthya 2012: Data-resolution based optimization of the data -collection strategy for near infrared diffuse optical tomography Med. Phys. 39 (8). DOI: 10.1118/1.4736820
22. 22.P. K. Yalavarthy, B. W. Pogue, H. Dehghani, C. M. Carpenter, S. Jiang and K. D. Paulsen 2007: Structural information within regularization matrices improves near infrared diffuse optical tomography Opt.Express,15, 8043–8058.https://doi.org/10.1364/OE.15.008043
23. 23.S. H. Katamreddy, P. K. Yalavarthy 2012: Model-resolution based regularization improves near infrared diffuse optical tomography J. Opt. Soc. Am. A, Vol. 29, No. 5.https://doi.org/10.1364/JOSAA.29.000649
24. 24.Ville Kolehmainen 2001: Computational Methods for Light Transport in Optical Tomography Ph.D. thesis Kuopio University Publications C. Natural and Environmental Sciences, p.125.https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.97.1872&rep=rep1&type=pdf
25. 25.Athanasios D. Zacharopoulos, Martin Schweiger, Ville Kolehmainen and Simon Arridge 2009: 3D shape based reconstruction of experimental data in Diffuse Optical Tomography Optics Express, Vol. 17, Issue 21, pp. 18940-18956 . DOI: 10.1088/0031-9155/49/12/n01
26. 26.C. S. Musgrove 2007: Issues related to the forward problem for endoscopic near-infrared diffuse optical tomography Thesis submitted to the Faculty of the Graduate College of Oklahoma State University, December.https://hdl.handle.net/11244/10248
27. 27.Intes X, Maloux C, Guven M, Yazici B, Chance B 2004: Diffuse optical tomography with physiological and spatial a priori constraints Phys Med Biol.,p 49(12):N155-63. doi: 10.1088/0031-9155/49/12/n01
28. 28.Huacheng Feng, Jing Bai, Xiaolei Song, Gang Hu, and Junjie Yao 2007: A Near-Infrared Optical Tomography System Based on Photomultiplier Tube International Journal of Biomedical Imaging Volume, 9 pages doi:10.1155/2007/28387
29. 29.Yudovsky D, Durkin AJ 2011: Spatial frequency domain spectroscopy of two layer mediaJ Biomed Opt. Oct;16(10):107005.https://doi.org/10.1364/OE.17.018940
30. 30.UmaMaheswari, K, Sathiyamoorthy, S, 2018:Fixed grid wavelet network segmentation on diffuse optical tomography image to detect sarcoma, Journal of Applied Research and Technology, Vol.16(2), pp.126-139.doi: 10.1109/42.918473
31. 31.Lizeng Sheng 2004: Finite Element Analysis and Genetic Algorithm Optimization Design for the Actuator Placement on a Large Adaptive Structure publisherBlacksburg,Va.: University Libraries, Virginia Polytechnic Institute and State University.https://vtechworks.lib.vt.edu/handle/10919/30184
32. 32.Behnoosh Tavakoli, Quing Zhu 2013: Two-step reconstruction method using global optimization and conjugate gradient for ultrasound-guided diffuse optical tomography J. Biomed. Opt. p.18 (1), 016000.https://doi.org/10.1117/1.JBO.18.1.016006
33. 33.Kwangmoo Koh, Seungjean Kim, Stephen Boyd, l1 ls 2008: A Matlab Solver for Large-Scale ℓ1-Regularized Least Squares Problems.https://web.stanford.edu/∼boyd/l1_ls/l1_ls_usrguide.pdf
34. 34.https://wiki.thayer.dartmouth.edu/.
35. 35.Dehghani H, Eames ME, Yalavarthy PK, Davis SC, Srinivasan S, Carpenter CM, et al. Near infrared optical tomography using NIRFAST: Algorithms for numerical model and image reconstruction. algorithms Commun. Numer. Methods Eng. 2009;25:711-732. DOI: 10.1002/cnm.1162
36. 36.www.hpfem.jku.at/netgen

Written By

Umamaheswari Kumarasamy, G.V. Shrichandran and A. Vedanth Srivatson

Submitted: May 24th, 2021Reviewed: June 3rd, 2021Published: June 30th, 2021