X-ray luminescence computed tomography (XLCT) is an emerging hybrid imaging modality that can provide functional and anatomical images at the same time. Traditional narrow beam XLCT can achieve high spatial resolution as well as high sensitivity. However, by treating the CCD camera as a single pixel detector, this kind of scheme resembles the first generation of CT scanner which results in a long scanning time and a high radiation dose. Although cone beam or fan beam XLCT has the ability to mitigate this problem with an optical propagation model introduced, image quality is affected because the inverse problem is ill-conditioned. Much effort has been done to improve the image quality through hardware improvements or by developing new reconstruction techniques for XLCT. The objective of this work is to further enhance the already reconstructed image by introducing anatomical information through retrospective processing. The deblurring process used a spatially variant point spread function (PSF) model and a joint entropy based anatomical prior derived from a CT image acquired using the same XLCT system. A numerical experiment was conducted with a real mouse CT image from the Digimouse phantom used as the anatomical prior. The resultant images of bone and lung regions showed sharp edges and good consistency with the CT image. Activity error was reduced by 52.3% even for nanophosphor lesion size as small as 0.8mm.
Chang Su, Joyita Dutta, Hui Zhang, Georges El Fakhri, and Quanzheng Li, "Image deblurring using a joint entropy prior in x-ray luminescence computed tomography," Proc. SPIE 10137, Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1013706 (Presented at SPIE Medical Imaging: February 12, 2017; Published: 13 March 2017); https://doi.org/10.1117/12.2254649.
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