Maximum a posteriori estimation for SPECT using regularizationtechniques on massively parallel computers
Butler, C.S.; Miller, M.I.
Medical Imaging, IEEE Transactions on
Volume 12, Issue 1, Mar 1993 Page(s):84 - 89
Digital Object Identifier 10.1109/42.222671
Summary:Single photon emission computed tomography (SPECT) reconstructions
performed using maximum a posteriori (penalized likelihood) estimation
with the expectation maximization algorithm are discussed. Due to the
large number of computations, the algorithms were performed on a
massively parallel single-instruction multiple-data computer.
Computation times for 200 iterations, using I.J. Good and R.A. Gaskins's
(1971) roughness as a rotationally invariant roughness penalty, are
shown to be on the order of 5 min for a 64×64 image with 96 view
angles on an AMT-DAP 4096 processor machine and 1 min on a MasPar 4096
processor machine. Computer simulations performed using parameters for
the Siemens gamma camera and clinical brain scan parameters are
presented to compare two regularization techniques-regularization by
kernel sieves and penalized likelihood with Good's rotationally
invariant roughness measure-to filtered backprojection. Twenty-five
independent sets of data are reconstructed for the pie and Hoffman brain
phantoms. The average variance and average deviation are examined in
various areas of the brain phantom. It is shown that while the geometry
of the area examined greatly affects the observed results, in all cases
the reconstructions using Good's roughness give superior variance and
bias results to the two alternative methods
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