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Fast scaled gradient decomposition methods for maximum likelihood transmission tomography

New iterative algorithms are presented for maximum likelihood (ML) and regularized maximum likelihood (MAP) reconstruction in transmission tomography (CT). The algorithms are natural extensions to CT of RAMLA, a well known method for ML reconstruction in emission computed tomography (ECT). We show that the new algorithm for ML solutions produces similar, or even better results than EM-like algorithms, but in much fewer iterations. Also, its convergence properties are better than other ordered subsets methods.