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. 2020 Jun 12;11(7):3717-3732.
doi: 10.1364/BOE.393970. eCollection 2020 Jul 1.

Adaptive shrinking reconstruction framework for cone-beam X-ray luminescence computed tomography

Haibo Zhang  1 , Xiaodong Huang  2 , Mingquan Zhou  1 , Guohua Geng  1 , Xiaowei He  1
Affiliations

Adaptive shrinking reconstruction framework for cone-beam X-ray luminescence computed tomography

Haibo Zhang et al. Biomed Opt Express. .

Abstract

Cone-beam X-ray luminescence computed tomography (CB-XLCT) emerged as a novel hybrid technique for early detection of small tumors in vivo. However, severe ill-posedness is still a challenge for CB-XLCT imaging. In this study, an adaptive shrinking reconstruction framework without a prior information is proposed for CB-XLCT. In reconstruction processing, the mesh nodes are automatically selected with higher probability to contribute to the distribution of target for imaging. Specially, an adaptive shrinking function is designed to automatically control the permissible source region at a multi-scale rate. Both 3D digital mouse and in vivo experiments were carried out to test the performance of our method. The results indicate that the proposed framework can dramatically improve the imaging quality of CB-XLCT.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Fig. 1.
Fig. 1.
Schematic diagram of CB-XLCT system [–12].
Fig. 2.
Fig. 2.
The multi-scale of ζ function with the increase of k.
Fig. 3.
Fig. 3.
Reconstruction model in simulation experiment. (a) 3D Mouse Model (b) Forward simulation of single-target case (c) Forward simulation of double-targets case (d) 2D view of CT slice for the single-target case (e) 2D view of CT slice for the double-targets case.
Fig. 4.
Fig. 4.
The 2D views (Z=50.8mm plane) of the reconstructed results for the single-target reconstruction.
Fig. 5.
Fig. 5.
The iteration number of ADS_PSR and ISPR with artificial threshold in single-target case.
Fig. 6.
Fig. 6.
2D views (Z=60mm plane) of the reconstructed results for the double-targets reconstruction.
Fig. 7.
Fig. 7.
The iteration number of ADS_PSR and ISPR with artificial threshold in double-targets case.
Fig. 8.
Fig. 8.
Illustration of LE and RD at different number of view angles
Fig. 9.
Fig. 9.
Illustration of LE and RD at different noise levels.
Fig. 10.
Fig. 10.
Illustration of LE and RD at different number of nodes.
Fig. 11.
Fig. 11.
The micro-CT result of the female mouse.
Fig. 12.
Fig. 12.
Reconstruction results overlaid with CT data of in vivo experiment. (a) coronal-view results at X=12mm. (b) sagittal-view results at Y=13.2mm; (c) transversal-view results at Z=6.8mm.
Fig. 13.
Fig. 13.
The iteration number of ADS_PSR and ISPR with artificial threshold of in vivo experiment.
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