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Penalized‐likelihood sinogram smoothing for low‐dose CT
Author(s) -
La Rivière Patrick J.
Publication year - 2005
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.1915015
Subject(s) - smoothing , spline (mechanical) , iterative reconstruction , artificial intelligence , computer science , mathematics , expectation–maximization algorithm , algorithm , mathematical optimization , computer vision , maximum likelihood , statistics , physics , thermodynamics
We have developed a sinogram smoothing approach for low‐dose computed tomography (CT) that seeks to estimate the line integrals needed for reconstruction from the noisy measurements by maximizing a penalized‐likelihood objective function. The maximization is performed by an algorithm derived by use of the separable paraboloidal surrogates framework. The approach overcomes some of the computational limitations of a previously proposed spline‐based penalized‐likelihood sinogram smoothing approach, and it is found to yield better resolution‐variance tradeoffs than this spline‐based approach as well an existing adaptive filtering approach. Such sinogram smoothing approaches could be valuable when applied to the low‐dose data acquired in CT screening exams, such as those being considered for lung‐nodule detection.