OBJECTIVE: X-ray luminescence computed tomography (XLCT) is an emerging and promising modality, but suffers from inferior reconstructions and smoothed target shapes. This work aims to improve the image quality with new mathematical framework. METHODS: We present a Bayesian local regularization framework to tackle the ill-conditioness of XLCT. Different from traditional overall regularization strategies, the proposed method utilizes correlations of neighboring voxels to regularize the solution locally based on generalized adaptive Gaussian Markov random field (GAGMRF), and provides an adjustable parameter to facilitate the edge-preserving property. RESULTS: Numerical simulations and phantom experiments show that the GAGMRF method yields both high image quality and accurate target shapes. CONCLUSION: Compared to conventional L2 and L1 regularizations, GAGMRF provides a new and efficient model for high quality imaging based on the Bayesian framework. SIGNIFICANCE: The GAGMRF method offers a flexible regularization framework to adapt to a wide range of biomedical applications.
OBJECTIVE: X-ray luminescence computed tomography (XLCT) is an emerging and promising modality, but suffers from inferior reconstructions and smoothed target shapes. This work aims to improve the image quality with new mathematical framework. METHODS: We present a Bayesian local regularization framework to tackle the ill-conditioness of XLCT. Different from traditional overall regularization strategies, the proposed method utilizes correlations of neighboring voxels to regularize the solution locally based on generalized adaptive Gaussian Markov random field (GAGMRF), and provides an adjustable parameter to facilitate the edge-preserving property. RESULTS: Numerical simulations and phantom experiments show that the GAGMRF method yields both high image quality and accurate target shapes. CONCLUSION: Compared to conventional L2 and L1 regularizations, GAGMRF provides a new and efficient model for high quality imaging based on the Bayesian framework. SIGNIFICANCE: The GAGMRF method offers a flexible regularization framework to adapt to a wide range of biomedical applications.