Application of Reconstruction Algorithms by Simulation Experiments for the Diagnosis of Breast Tumor-Like Tissues Modeled in Diffuse Optical Tomography
Abstract
In Diffuse Optical Tomography (DOT), data processing and reconstruction stages are crucial to obtain high-quality images. Thus, choosing suitable algorithms for the system is a critical choice. This study aims to determine an appropriate reconstruction algorithm for DOT imaging. There are several reconstruction algorithms used in DOT systems. Some algorithms have been improved for solving specific cases, and some still need to be improved. In this study, we used three algorithms for the reconstruction process: Singular Value Decomposition (SVD), Bi-Conjugated Gradient (Bi-CG), and Transpose Free Quasi Minimal Residual (TFQMR). In testing the algorithms, data of the simulation experiments have been used. The simulation experiments model the tumoral tissue within the breast. All three algorithms were produced correct images while the tumor close to the surface. In the case of the tumor that is not close to the breast surface, the tumor location on the images created by Bi-CG and SVD algorithms was not its actual location. However, the tumor location in the image created by the TFQMR algorithm was close to its actual location. Outcomes of the reconstruction algorithms were evaluated based on correctly defining the location of the tumors by using Mean Percentage Error (MPE), Mean Squared Error (MSE), and Mean Absolute Error (MAE) metrics. We have demonstrated the TFQMR algorithm is a more appropriate reconstruction technique for DOT systems. Thus, we have concluded that TFQMR can have the potential to be used in medical imaging systems.
Keywords
Reconstruction Algorithm, Singular Value Decomposition, Bi-Conjugated Gradient, Transpose Free Quasi Minimal Residual
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