1. Tianshuai Liu1, Junyan Rong1, Peng Gao1, Hongbing Lu1
Cone-Beam X-ray Luminescence Computed Tomography Based on X-ray Absorption Dosage
Tianshuai Liu1, Junyan Rong1, Peng Gao1, Hongbing Lu1
1. Department of Biomedical Engineering, Fourth Military Medical University, China
Introduction
Based on X-ray excitable particles, cone-beam X-ray luminescence computed tomography (CB-XLCT) has been proposed recently [1-3], which aims to achieve high- sensitivity optical imaging as well as high spatial resolution X-ray imaging. Currently, the imaging model of most XLCT systems is derived from the intensity distribution of X-ray within the object [4,5], not completely reflecting the nature of X-ray excitation process. To further improve the imaging quality of CB-XLCT, in this study, an imaging model based on X-ray absorption dosage is proposed. Imaging experiments with numerical simulations and a physical phantom indicate that when compared with the model based on X-ray intensity, the proposed model based on X- ray dosage improves the image quality of CB-XLCT significantly.
Experimental Design
Simulation with a cylinder phantom
Figure 2. The simulation of a target with different depths.
Method
Fig 1 gives a schematic diagram of the CB-XLCT system used in this study.
We suppose that the excitation model based on X-ray dosage can better reflect the nature of X-ray excitation process. Under this assumption, the proposed model can be expressed as:
Result
For the targets positioned at three different depths, the XLCT tomographic images reconstructed from simulated projections ions are shown in Fig 3.
XLCT D XLCT D
Figure 1. The schematic diagram of the CB-XLCT system.
Figure 3. CB-XLCT reconstructions based on the X-ray intensity model (left) and the X-ray dose model (right).
The reconstructions of the phantom in the real experiment are in Fig 4.
XLCT Fusion D
where S(r) is the light emitted, Xd(r) is the absorbed dose of X-rays at position r, which is estimated by simulation with the widely-used GATE package.
Figure 4. Tomographic images of the physical phantom reconstructed using different forward models. Upper: the X-ray intensity model. Lower: the X-ray dose model
The propagation model of the emitted light in biological tissues can be established by the diffusion equation (DE). By using the finite element method (FEM), the forward model can be expressed as:
References
[1] G. Pratx, et al., IEEE Trans Med Imaging, 29, 1992, (2010).
[2] D. Chen, et al., Med Phys, 40, , ( 2013).
[3] X. Liu, et al., IEEE Trans Biomed Eng, 61, 1621, (2014).
[4] W. Cong, et al., J Biomed Opt, 16, , (2011).
[5] G. Zhang, et al., IEEE Trans Med Imaging, 1-1, 99, (2016).
where Φs is the photon fluence vector measured on the object surface, N is the nanophosphor concentration vector.
Then, an adaptive Tikhonov regularization method is adopted to solve the CB-XLCT inverse problem.