Back-propagation neural network-based reconstruction algorithm for diffuse optical tomography.

Jinchao Feng,Zhe Li,Qiuwan Sun,Kebin Jia,Zhonghua Sun

doi:10.1117/1.jbo.24.5.051407

Abstract

.Diffuse optical tomography (DOT) is a promising noninvasive imaging modality and is capable of providing functional characteristics of biological tissue by quantifying optical parameters. The DOT image reconstruction is ill-posed and ill-conditioned, due to the highly diffusive nature of light propagation in biological tissues and limited boundary measurements. The widely used regularization technique for DOT image reconstruction is Tikhonov regularization, which tends to yield oversmoothed and low-quality images containing severe artifacts. It is necessary to accurately choose a regularization parameter for Tikhonov regularization. To overcome these limitations, we develop a noniterative reconstruction method, whereby optical properties are recovered based on a back-propagation neural network (BPNN). We train the parameters of BPNN before DOT image reconstruction based on a set of training data. DOT image reconstruction is achieved by implementing a single evaluation of the trained network. To demonstrate the performance of the proposed algorithm, we compare with the conventional Tikhonov regularization-based reconstruction method. The experimental results demonstrate that image quality and quantitative accuracy of reconstructed optical properties are significantly improved with the proposed algorithm.

Highlights

Diffuse optical tomography (DOT) has shown a great potential for breast imaging[1,2,3,4,5,6,7,8,9] and functional brain imaging,[10,11,12] which use near-infrared light in the spectral range of 600 to 950 nm to quantify tissue optical coefficients
These results show that back-propagation neural network (BPNN) outperforms Tikhonov regularization in terms of higher accuracy and better
We explored using a BPNN to recover optical properties to improve the reconstruction accuracy and image quality of DOT

Summary

Introduction

Diffuse optical tomography (DOT) has shown a great potential for breast imaging[1,2,3,4,5,6,7,8,9] and functional brain imaging,[10,11,12] which use near-infrared light in the spectral range of 600 to 950 nm to quantify tissue optical (absorption and scattering) coefficients. Recovering the internal distribution of optical properties is a severely ill-posed and under-determined inverse problem, due to light propagation in highly scattering biological tissues and limited number of measurements,[13,14] which makes image reconstruction challenging Both linear and nonlinear reconstruction algorithms for DOT are available,[14] considerable efforts have been made to develop various reconstruction algorithms to improve quantitative accuracy and image quality.[14,15,16,17,18,19,20,21,22] To date, the illposedness of the inverse problem in DOT can be alleviated by employing a regularization technique, which utilizes a data fitting term together with a regularizer (L2 or L1 norm, etc.) to suppress the effect of measurement noise and modeling errors.[23]. The quality of reconstructed images can be improved with the use of sparsity regularization.[23,25]

Methods

Results

Conclusion