The expectation maximization (EM) algorithm for the maximum likelihood (ML) image reconstruction criterion generates severe checkerboard artifacts in the presence of noise. A classical remedy is to impose an a priori constraint for a penalized ML or maximum a posteriori probability solution. The penalty reduces the checkerboard artifacts and also introduces uncertainty because a priori information is usually unknown in clinic. Recent theoretical investigation reveals that the noise can be divided into two components. One is called null-space noise which annihilates during filtered backprojection (FBP)-type analytical image reconstruction. The other is called range-space noise which propagates into the FBP-type analytically reconstructed image. In particular, the null-space noise can be numerically estimated. The aim of this work is to investigate the relation between the null-space noise and the checkerboard artifacts in the ML-EM image reconstruction from noise projection data. It is expected that removing the null-space noise from the projection data could improve the signal-to-noise ratio of the data and, therefore, reduce the checkerboard artifacts in the ML-EM reconstructed images. The expectation was realized by computer simulation studies with application to single photon emission computed tomography, where the noise has been a major factor for image degradation. The reduction of the ML-EM checkerboard artifacts by removing the null-space noise avoids the uncertainty of using a priori penalty.