Accelerated convergence with image-block iterative reconstruction

It is known that the convergence of iterative algorithms is better when updating pixels sequentially rather than simultaneously, but sequential updating is more computationally demanding and less practical for implementation issues. We propose to divide the image in a matrix of equally sized cuboids and update them separately as groups of pixels in a grouped coordinate ascent algorithm. We apply this to transmission and emission tomography. For both applications we see a substantial increase in convergence per iteration.