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Grouped-coordinate ascent algorithms for penalized-likelihood transmission image reconstruction

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4 Author(s)
Fessler, J.A. ; Michigan Univ., Ann Arbor, MI, USA ; Ficaro, E.P. ; Clinthorne, N.H. ; Lange, Kenneth

Presents a new class of algorithms for penalized-likelihood reconstruction of attenuation maps from low-count transmission scans. We derive the algorithms by applying to the transmission log-likelihood a version of the convexity technique developed by De Pierro for emission tomography. The new class includes the single-coordinate ascent (SCA) algorithm and Lange's convex algorithm for transmission tomography as special cases. The new grouped-coordinate ascent (GCA) algorithms in the class overcome several limitations associated with previous algorithms. (1) Fewer exponentiations are required than in the transmission maximum likelihood-expectation maximization (ML-EM) algorithm or in the SCA algorithm. (2) The algorithms intrinsically accommodate nonnegativity constraints, unlike many gradient-based methods. (3) The algorithms are easily parallelizable, unlike the SCA algorithm and perhaps line-search algorithms. We show that the GCA algorithms converge faster than the SCA algorithm, even on conventional workstations. An example from a low-count positron emission tomography (PET) transmission scan illustrates the method.

Published in:

Medical Imaging, IEEE Transactions on  (Volume:16 ,  Issue: 2 )

Date of Publication:

April 1997

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