An iterative weighted method based on YALL1 for cone-beam X-ray luminescence optical tomography imaging: A phantom experimental study

Cone-beam X-ray luminescence optical tomography (CB-XLOT) plays an important role in in vivo small animal imaging study, which can non-invasively image the three-dimensional (3-D) distribution of x-ray-excitable nanophosphors deeply embedded in imaged object. However, CB-XLOT suffers from a low spatial resolution due to the ill-posed nature of optical reconstruction. To alleviate the ill-posedness of reconstruction and improve the imaging performance of XLOT, in this paper, we propose an iterative weighted L1 minimization method which is achieved by incorporating YALL1 (Your algorithm for L1 norm problems). The physical phantom experiment was conducted to evaluate the performance of the proposed method, where a custom-made cone-beam XLOT system was used as the imaging platform. The experimental results indicate that by applying the proposed iterative weighted strategy to YALL1 method, the reconstruction performance of XLOT can be improved when compared with the conventional YALL1 method.

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