Maximum likelihood, least squares and penalized least squares for PET

Journal Article · Tue Jun 01 04:00:00 EDT 1993 · IEEE Transactions on Medical Imaging (Institute of Electrical and Electronics Engineers); (United States)

DOI:https://doi.org/10.1109/42.232249· OSTI ID:6145763

Kaufman, L ^[1]

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1. AT and T Bell Labs., Murray Hill, NJ (United States)

The EM algorithm is the basic approach used to maximize the log likelihood objective function for the reconstruction problem in PET. The EM algorithm is a scaled steepest ascent algorithm that elegantly handles the nonnegativity constraints of the problem. The authors show that the same scaled steepest descent algorithm can be applied to the least squares merit function, and that it can be accelerated using the conjugate gradient approach. The experiments suggest that one can cut the computation by about a factor of 3 by using this technique. The results also apply to various penalized least squares functions which might be used to produce a smoother image.

• OSTI ID: 6145763
• Journal Information: IEEE Transactions on Medical Imaging (Institute of Electrical and Electronics Engineers); (United States), Journal Name: IEEE Transactions on Medical Imaging (Institute of Electrical and Electronics Engineers); (United States) Vol. 12:2; ISSN 0278-0062; ISSN ITMID4
• Country of Publication: United States
• Language: English

Related Subjects

550601* -- Medicine-- Unsealed Radionuclides in Diagnostics
62 RADIOLOGY AND NUCLEAR MEDICINE
ACCURACY
ALGORITHMS
COMPUTERIZED TOMOGRAPHY
DIAGNOSTIC TECHNIQUES
EMISSION COMPUTED TOMOGRAPHY
LEAST SQUARE FIT
MATHEMATICAL LOGIC
MAXIMUM-LIKELIHOOD FIT
NUMERICAL SOLUTION
POSITRON COMPUTED TOMOGRAPHY
RELIABILITY
TOMOGRAPHY