Cone-beam x-ray luminescence computed tomography based on x-ray absorption dosage

Tianshuai Liu; Junyan Rong; Peng Gao; Wenli Zhang; Wenlei Liu; Yuanke Zhang; Hongbing Lu

doi:10.1117/1.JBO.23.2.026006

22 February 2018 Cone-beam x-ray luminescence computed tomography based on x-ray absorption dosage

Tianshuai Liu, Junyan Rong, Peng Gao, Wenli Zhang, Wenlei Liu, Yuanke Zhang, Hongbing Lu

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Journal of Biomedical Optics, Vol. 23, Issue 2, 026006 (February 2018). https://doi.org/10.1117/1.JBO.23.2.026006

Abstract

With the advances of x-ray excitable nanophosphors, x-ray luminescence computed tomography (XLCT) has become a promising hybrid imaging modality. In particular, a cone-beam XLCT (CB-XLCT) system has demonstrated its potential in in vivo imaging with the advantage of fast imaging speed over other XLCT systems. Currently, the imaging models of most XLCT systems assume that nanophosphors emit light based on the intensity distribution of x-ray within the object, not completely reflecting the nature of the x-ray excitation process. To improve the imaging quality of CB-XLCT, an imaging model that adopts an excitation model of nanophosphors based on x-ray absorption dosage is proposed in this study. To solve the ill-posed inverse problem, a reconstruction algorithm that combines the adaptive Tikhonov regularization method with the imaging model is implemented for CB-XLCT reconstruction. Numerical simulations and phantom experiments indicate that compared with the traditional forward model based on x-ray intensity, the proposed dose-based model could improve the image quality of CB-XLCT significantly in terms of target shape, localization accuracy, and image contrast. In addition, the proposed model behaves better in distinguishing closer targets, demonstrating its advantage in improving spatial resolution.

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Tianshuai Liu, Junyan Rong, Peng Gao, Wenli Zhang, Wenlei Liu, Yuanke Zhang, and Hongbing Lu "Cone-beam x-ray luminescence computed tomography based on x-ray absorption dosage," Journal of Biomedical Optics 23(2), 026006 (22 February 2018). https://doi.org/10.1117/1.JBO.23.2.026006

Received: 12 October 2017; Accepted: 22 January 2018; Published: 22 February 2018

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