MASSIVELY-PARALLEL COMPUTERS FOR 3D SINGLE-PHOTON-EMISSION COMPUTED-TOMOGRAPHY

Since the introduction of the expectation-maximization (EM) algorithm for generating maximum-likelihood (ML) and maximum a posteriori (MAP) estimates in emission tomography, there have been many investigators a pplying the ML method. However, almost all of the previous work has be en restricted to two-dimensional (2D) reconstructions. The major focus and contribution of this paper is to demonstrate a fully three-dimens ional (3D) implementation of the MAP method for single-photon-emission computed tomography (SPECT). The 3D reconstruction exhibits an improv ement in resolution when compared to the generation of the series of s eparate 2D slice reconstructions. As has been noted, the iterative EM algorithm for 2D reconstruction is highly computational; the 3D algori thm is far worse. To accommodate the computational complexity, we have extended our previous work in the 2D arena and demonstrate an impleme ntation on the class of massively parallel processors of the 3D algori thm. Using a 16 000 processor MasPar machine, the algorithm is demonst rated to execute at 1.24 s/EM iteration for the entire 64 x 64 x 64 cu be of 64 planar measurements obtained from the Siemens Orbiter rotatin g camera operating in the high-resolution mode.