Abstract
In recent years, cone-beam X-ray luminescence computed tomography (CB-XLCT) has drawn much attention with the development of X-ray excited nanophosphors. Compared with traditional bio-optical imaging modalities such as bioluminescence tomography (BLT) and fluorescence molecular tomography (FMT), CB-XLCT can effectively improve imaging sensitivity and depth because of the reduction of background fluorescence and the high penetrability of X-rays. However, due to high degree of scattering of light through biological tissues, the reconstruction of CB-XLCT is inherently ill-conditioned. To solve the ill-posed inverse problem, appropriate priors or regularizations are needed to facilitate the reconstruction. Based on the fact that adjacent pixels generally have the same or similar concentration and in order to further balance the degree of regional smoothness and edge sharpening, a prior information model based on Huber Markov Random Field (HuMRF) was established to constrain the reconstruction process of CB-XLCT. Mice experiments indicate that compared with the traditional ART and ADAPTIK method, the proposed method could improve the image quality of CB-XLCT significantly in terms of target shape, localization accuracy and image contrast.
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