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Analysis reveals noise affects algorithms in emission computed tomography, indicating challenges in convergence.
We study the influence of noise in the EM algorithm and ISRA. Both methods have been used for emission computed tomography. In the underlying linear model Ax~ b A x ∼ b the noise can be in the data vector b and/or in the matrix A . Under certain conditions it is shown that the noise error asymptotically grows like O(√k) O ( k ) , with k the iteration index. For ISRA we also introduce a relaxation parameter and give conditions under which convergence is maintained.
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Tommy Elfving (2026) studied this question.
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