**Abstract**

Diffuse optical tomography (DOT) is favorable to analyze physical records in organic tissue with a specific purpose by means of a method related to the forward problem and the inverse solution. This study develops morphological soft tissue realization using an image reconstruction algorithm constructed on multifrequency DOT in Near-Infra-Red (NIR) wavelength. Forward problem solves the Diffusion Equation to compute the optical flux distributed in the phantom geometrical model. Inverse solution, the image is reconstructed using the absorption and reduced scattered coefficients under different boundary conditions. The inverse image reconstruction algorithm is tested for several simulation, with variation in background contrast ratios for different frequencies are simulated. The image reconstruction in DOT eliminates spatial resolution by optimizing source-detector separation and modulation intensities of the source.

**Keywords:** diffuse optical tomography, near infrared wavelength, forward model, inverse model, soft tissue, image reconstruction

### **1. Introduction**

Forward problems are used to explain the propagation of photons within a tissue and to calculate the optical flux at the tissue boundary. The reconstruction of the tissue image is the inverse problem, from light measurements at the boundary phantom surface, tissue absorption, scattering factors, and optical flow [1]. The inverse problem is difficult to solve due to the issue of fairness. This indicates that the problem is not properly configured. Appropriate problem characteristics include the existence of a solution, the uniqueness of the solution, and the constant reliance on the data [2]. The third property determines the stability of the solution and is important for determining the inverse problem. The ill-posedness problem occurs when the problem solution does not depend on the data indefinitely. Small changes to the data can make a big difference in the solution in this case. Regularization method is used to solve this problem, which is a regularization method that introduces additional information in order to create well-posed data [3, 4]. Diffuse optical imaging [5–8] is a technique that uses an MRI scan and X-rays generate spatially decomposed images and uses high-resolution complementary structural information to improve lowresolution functional images. A set of fiber optics has been connected the object's boundary in experimental systems. The light source was a near-infrared (NIR) laser

source that was diffused on the phantom, the scattered rays were measured with a photodetector [9]. Regularization method [10] is used to remove the ill-posedness, with the Levenberg–Marquardt method (LM) being one of the most commonly used methods. Following the regularization process, the Split Bregman reconstruction method [10–12] is used to reconstruct a soft tissue image. The sparsity regularization technique for image reconstruction in DOT is described by Bo Bi et al. [9]. Gehre et al. [13] investigates the possibility of sparsity constraints in the inverse problem of deriving distributed conductivity from critical potential measurements in electrical impedance tomography (EIT). Chamorro et al. [14] proposed an Algebraic reconstruction technique—Split Bregman (ART-SB) algorithm solved the L1-regularized problem. Wang et al. evaluated the Split Bregman iteration algorithm for the L1 norm regularization inverse problem in electrical impedance tomography. Figueiredo et al. [15] investigated the use of Split Bregman iterative algorithms for the L1-norm regularized inverse problem of electrical impedance constrained quadratic programming ill-tomography formulation.
