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Photonics 2014, 1(2), 95-109; doi:10.3390/photonics1020095

Comparison of Regularization Methods in Fluorescence Molecular Tomography

1
School of Engineering, University of California Merced, Merced, CA 95343, USA
2
Bioimaging Core, Faculty of Health Sciences, University of Macau, Av. Padre Tomás Pereira, Taipa, Macau SAR, China
*
Author to whom correspondence should be addressed.
Received: 1 February 2014 / Revised: 14 April 2014 / Accepted: 17 April 2014 / Published: 29 April 2014
(This article belongs to the Special Issue Biomedical Optics and Optical Imaging)
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Abstract

In vivo fluorescence molecular tomography (FMT) has been a popular functional imaging modality in research labs in the past two decades. One of the major difficulties of FMT lies in the ill-posed and ill-conditioned nature of the inverse problem in reconstructing the distribution of fluorophores inside objects. The popular regularization methods based on L2, L1 and total variation (TV ) norms have been applied in FMT reconstructions. The non-convex Lq(0 < q < 1) semi-norm and Log function have also been studied recently. In this paper, we adopt a uniform optimization transfer framework for these regularization methods in FMT and compare their individual, as well as the combined effects on both small, localized targets, such as tumors in the early stage, and large targets, such as liver. Numerical simulation studies and phantom experiments have been carried out, and we found that Lq with q near 1/2 performs the best in reconstructing small targets, while joint L2 and Log performs the best for large targets. View Full-Text
Keywords: reconstruction techniques; optics; fluorescence molecular tomography; optimization transfer reconstruction techniques; optics; fluorescence molecular tomography; optimization transfer
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Zhu, D.; Zhao, Y.; Baikejiang, R.; Yuan, Z.; Li, C. Comparison of Regularization Methods in Fluorescence Molecular Tomography. Photonics 2014, 1, 95-109.

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