Title :
Recursive least squares algorithm for optical diffusion tomography
Author :
Guven, Murat ; Yazici, Birsen ; Intes, Xavier ; Chance, Britton ; Zheng, Yibin
Author_Institution :
Drexel University
Abstract :
Algebraic reconstruction techniques (ART) is a family of practical algorithms which sets algebraic equations for the unknowns in terms of the measured data and solves these equations iteratively. It is typical that the system of linear equations obtained in Diffuse Optical Tomography (DOT) is underdetermined and/or ill-conditioned. ART is one of the most popular image reconstruction techniques used in DOT to solve this kind of system of linear equations. There is, however, no natural way of including a priori information about the image in ART algorithm. Moreover ART requires a large number of iterations to reconstruct the image and hence convergence to the solution is slow. In this paper, for the inverse problem in DOT, we apply a Recursive Least Squares Algorithm (IUS) that converges in only one iteration and enables the use of a priori information such as image smoothness.We present comparison between the images reconstructed by ART and IUS.
Keywords :
Equations; Image converters; Image reconstruction; Inverse problems; Iterative algorithms; Least squares methods; Resonance light scattering; Subspace constraints; Tomography; US Department of Transportation;
Conference_Titel :
Bioengineering Conference, 2002. Proceedings of the IEEE 28th Annual Northeast
Conference_Location :
Philadelphia, PA, USA
Print_ISBN :
0-7803-7419-3
DOI :
10.1109/NEBC.2002.999571