Abstract An important feature of positron emission tomography (PET) and single photon emission computer tomography (SPECT) is the stochastic property of real clinical data. Statistical algorithms such as ordered subset-expectation maximization (OSEM) and maximum a posteriori (MAP) are a direct consequence of the stochastic nature of the data. The principal difference between these two algorithms is that OSEM is a non-regularized approach, while the MAP is a regularized algorithm. From the theoretical point of view, reconstruction problems belong to the class of ill-posed problems and should be considered using regularization. Regularization introduces an additional unknown regularization parameter into the reconstruction procedure as compared with non-regularized algorithms. However, a comparison of non-regularized OSEM and regularized MAP algorithms with fixed regularization parameters has shown very minor difference between reconstructions. This problem is analyzed in the present paper. To improve the reconstruction quality, a method of local regularization is proposed based on the spatially adaptive regularization parameter. The MAP algorithm with local regularization was tested in reconstruction of the Hoffman brain phantom.