2 May 2017 Anatomical image-guided fluorescence molecular tomography reconstruction using kernel method
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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.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Reheman Baikejiang, Reheman Baikejiang, Yue Zhao, Yue Zhao, Brett Z. Fite, Brett Z. Fite, Katherine W. Ferrara, Katherine W. Ferrara, Changqing Li, Changqing Li, } "Anatomical image-guided fluorescence molecular tomography reconstruction using kernel method," Journal of Biomedical Optics 22(5), 055001 (2 May 2017). https://doi.org/10.1117/1.JBO.22.5.055001 . Submission: Received: 5 January 2017; Accepted: 10 April 2017
Received: 5 January 2017; Accepted: 10 April 2017; Published: 2 May 2017

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