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Journal of Biomedical Optics

Anatomical image-guided fluorescence molecular tomography reconstruction using kernel method
Author(s): Reheman Baikejiang; Yue Zhao; Brett Z. Fite; Katherine W. Ferrara; Changqing Li
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Paper Abstract

Fluorescence molecular tomography (FMT) is an important in vivo imaging modality to visualize physiological and pathological processes in small animals. However, FMT reconstruction is ill-posed and ill-conditioned due to strong optical scattering in deep tissues, which results in poor spatial resolution. It is well known that FMT image quality can be improved substantially by applying the structural guidance in the FMT reconstruction. An approach to introducing anatomical information into the FMT reconstruction is presented using the kernel method. In contrast to conventional methods that incorporate anatomical information with a Laplacian-type regularization matrix, the proposed method introduces the anatomical guidance into the projection model of FMT. The primary advantage of the proposed method is that it does not require segmentation of targets in the anatomical images. Numerical simulations and phantom experiments have been performed to demonstrate the proposed approach’s feasibility. Numerical simulation results indicate that the proposed kernel method can separate two FMT targets with an edge-to-edge distance of 1 mm and is robust to false-positive guidance and inhomogeneity in the anatomical image. For the phantom experiments with two FMT targets, the kernel method has reconstructed both targets successfully, which further validates the proposed kernel method.

Paper Details

Date Published: 2 May 2017
PDF: 13 pages
J. Biomed. Opt. 22(5) 055001 doi: 10.1117/1.JBO.22.5.055001
Published in: Journal of Biomedical Optics Volume 22, Issue 5
Show Author Affiliations
Reheman Baikejiang, Univ. of California, Merced (United States)
Yue Zhao, Univ. of California, Merced (United States)
Brett Z. Fite, Univ. of California, Davis (United States)
Katherine W. Ferrara, Univ. of California, Davis (United States)
Changqing Li, Univ. of California, Merced (United States)


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