High-speed computation of the EM algorithm for PET image reconstruction

The PET image reconstruction based on the EM algorithm has several attractive advantages over the conventional convolution backprojection algorithms. However, two major drawbacks have impeded the routine use of the EM algorithm, namely, the long computational time due to slow convergence and the large memory required for the storage of the image, projection data and the probability matrix. Here, the authors attempt to solve these two problems by parallelizing the EM algorithm on a multiprocessor system. They have implemented an extended hypercube (EH) architecture for the high-speed computation of the EM algorithm using the commercially available fast floating point digital signal processor (DSP) chips as the processing elements (PEs). The authors discuss and compare the performance of the EM algorithm on a 386/387 machine, CD 4360 mainframe, and on the EH system. The results show that the computational speed performance of an EH(3,1) using DSP chips as PEs executing the EM image reconstruction algorithm is about 130 times better than that of the CD 4360 mainframe. The EH topology is expandable with more number of PEs