Prior Compensation Algorithm for Cerenkov Luminescence Tomography From Single-View Measurements

Abstract

Cerenkov luminescence tomography (CLT) has attracted much attention because of the wide clinically-used probes and three-dimensional (3D) quantification ability. However, due to the serious morbidity of 3D optical imaging, the reconstructed images of CLT are not appreciable, especially when single-view measurements are used. Single-view CLT improves the efficiency of data acquisition. It is much consistent with the actual imaging environment of using commercial imaging system, but bringing the problem that the reconstructed results will be closer to the animal surface on the side where the single-view image is collected. To avoid this problem to the greatest extent possible, we proposed a prior compensation algorithm for CLT reconstruction based on depth calibration strategy. This method takes full account of the fact that the attenuation of light in the tissue will depend heavily on the depth of the light source as well as the distance between the light source and the detection plane. Based on this consideration, a depth calibration matrix was designed to calibrate the attenuation between the surface light flux and the density of the internal light source. The feature of the algorithm was that the depth calibration matrix directly acts on the system matrix of CLT reconstruction, rather than modifying the regularization penalty items. The validity and effectiveness of the proposed algorithm were evaluated with a numerical simulation and a mouse-based experiment, whose results illustrated that it located the radiation sources accurately by using single-view measurements.

Keywords: Cerenkov luminescence tomography (CLT); cancer; optical imaging (OI); prior compensation; tomographic reconstruction.

Figure 1

Schematic diagrams of the simulation model indicating where the single-view image was collected (A) and of the definition of the depth (B).

Figure 2

Reconstructed results obtained by the proposed and reference methods. (A–C) The reconstructed results of the reference method at depth of 1 cm, 3 cm and 5 cm respectively; (D–F) The reconstructed results of the proposed method at depth of 1 cm, 3 cm and 5 cm respectively. The blue solid spheres mark the actual distribution of the mimic radioactive tracer, and the colored tetrahedrons are the reconstructed source.

Figure 3

Quantitative analysis of the reconstruction results. The calculated values of the indicators of Dis_err (A) and Dep_err (B). The black bars show the distance or depth errors of the reconstructed results by the reference method, and the red bars represent those obtained by the proposed method.

Figure 4

The reconstructed results and related quantitative analysis for the mouse based in vivo experiment. (A) Physical model of the mouse; (B) Light flux distribution on the mouse surface. (C, D) The 3D view of reconstructed images obtained by the reference method and the proposed method respectively; The long blue cylinder is the actual light source obtained by μCT, and the colored tetrahedrons are the reconstructed source; (E, F) The cross-sectional view of reconstructed images obtained by the reference method and the proposed method respectively. (G) The quantitative analysis of these reconstructed results. The black bars represent the quantitative indicators of the reference method, and the red bars are those of the proposed method.