Fast multislice fluorescence molecular tomography using sparsity-inducing regularization

Sedigheh Marjaneh Hejazi; Saeed Sarkar; Ziba Darezereshki

doi:10.1117/1.JBO.21.2.026012

26 February 2016 Fast multislice fluorescence molecular tomography using sparsity-inducing regularization

Sedigheh Marjaneh Hejazi, Saeed Sarkar, Ziba Darezereshki

Author Affiliations +

Funded by: Tehran University of Medical Sciences and the Iran Nanotechnology Initiative Council

Journal of Biomedical Optics, Vol. 21, Issue 2, 026012 (February 2016). https://doi.org/10.1117/1.JBO.21.2.026012

Abstract

Fluorescence molecular tomography (FMT) is a rapidly growing imaging method that facilitates the recovery of small fluorescent targets within biological tissue. The major challenge facing the FMT reconstruction method is the ill-posed nature of the inverse problem. In order to overcome this problem, the acquisition of large FMT datasets and the utilization of a fast FMT reconstruction algorithm with sparsity regularization have been suggested recently. Therefore, the use of a joint L1/total-variation (TV) regularization as a means of solving the ill-posed FMT inverse problem is proposed. A comparative quantified analysis of regularization methods based on L1-norm and TV are performed using simulated datasets, and the results show that the fast composite splitting algorithm regularization method can ensure the accuracy and robustness of the FMT reconstruction. The feasibility of the proposed method is evaluated in an in vivo scenario for the subcutaneous implantation of a fluorescent-dye-filled capillary tube in a mouse, and also using hybrid FMT and x-ray computed tomography data. The results show that the proposed regularization overcomes the difficulties created by the ill-posed inverse problem.

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Sedigheh Marjaneh Hejazi, Saeed Sarkar, and Ziba Darezereshki "Fast multislice fluorescence molecular tomography using sparsity-inducing regularization," Journal of Biomedical Optics 21(2), 026012 (26 February 2016). https://doi.org/10.1117/1.JBO.21.2.026012

Published: 26 February 2016

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