Entropy maximization algorithm for positron emission tomography

The expectation maximization (EM) algorithm is extensively used for tomographic image reconstruction based on positron emission tomography (PET) modality. The EM algorithm gives good reconstructed images compared to those created by deterministic methods such as filtered back projection (FBP) and convolution back projection (CBP). However, the computational complexity of EM-based algorithm is high due to the iterative nature of the algorithm. Prior knowledge of the estimate has been added to the basic EM algorithm to improve image quality as well as to reduce the number of iterations required for an acceptable image quality. We have developed an algorithm which produces better quality images in much lesser number of iterations, thereby speeding up the image reconstruction task.