Title :
A VLSI-inspired image reconstruction algorithm for continuous-wave diffuse optical tomography systems
Author :
Hsu, Yuan-Huang ; Fu, Chih-Chung ; Fang, Wai-Chi ; Sang, Tzu-Hsien
Author_Institution :
Dept. of Electron. Eng., Nat. Chiao Tung Univ., Hsinchu
Abstract :
In recent years, there has been an increasing interest in diffuse optical tomography (DOT). The image accuracy and computational complexity are greatly affected by the reconstruction algorithm. This paper aims at providing an efficient reconstruction technique which can be implemented in VLSI for advanced portable devices. Various inhomogeneous absorption models are used in simulations of continuous-wave DOT systems. The accuracy and time consumption are calculated and compared for both frame and sub-frame modes. Mean square errors (MSE) are derived to quantify the accuracy of different modes. Decreasing the complexity of algorithm and enhancing the quality of image can be achieved by adjusting the truncated parameter and reconstructive mode. This study has shown that the sub-frame mode with truncated Jacobi Singular Value Decomposition (TJSVD) algorithm is the method with higher efficiency and better quality. It can also be implemented in VLSI for continuous-wave DOT.
Keywords :
Jacobian matrices; VLSI; computational complexity; image reconstruction; medical image processing; optical tomography; singular value decomposition; VLSI inspired image reconstruction algorithm; computational complexity; continuous wave diffuse optical tomography; image accuracy; truncated Jacobi Singular Value Decomposition; Absorption; Computational complexity; Computational modeling; Image reconstruction; Jacobian matrices; Mean square error methods; Reconstruction algorithms; Tomography; US Department of Transportation; Very large scale integration;
Conference_Titel :
Life Science Systems and Applications Workshop, 2009. LiSSA 2009. IEEE/NIH
Conference_Location :
Bethesda, MD
Print_ISBN :
978-1-4244-4292-8
Electronic_ISBN :
978-1-4244-4293-5
DOI :
10.1109/LISSA.2009.4906716