E Krestyannikov et al 2008 Phys. Med. Biol. 53 2877 doi:10.1088/0031-9155/53/11/008
E Krestyannikov, J Tohka and U Ruotsalainen
Show affiliationsThis paper presents a novel statistical approach for joint estimation of regions-of-interest (ROIs) and the corresponding time–activity curves (TACs) from dynamic positron emission tomography (PET) brain projection data. It is based on optimizing the joint objective function that consists of a data log-likelihood term and two penalty terms reflecting the available a priori information about the human brain anatomy. The developed local optimization strategy iteratively updates both the ROI and TAC parameters and is guaranteed to monotonically increase the objective function. The quantitative evaluation of the algorithm is performed with numerically and Monte Carlo-simulated dynamic PET brain data of the 11C-Raclopride and 18F-FDG tracers. The results demonstrate that the method outperforms the existing sequential ROI quantification approaches in terms of accuracy, and can noticeably reduce the errors in TACs arising due to the finite spatial resolution and ROI delineation.
87.57.uk Positron emission tomography (PET)
02.50.Ng Distribution theory and Monte Carlo studies
Issue 11 (7 June 2008)
Received 10 December 2007, in final form 19 March 2008
Published 6 May 2008
E Krestyannikov et al 2008 Phys. Med. Biol. 53 2877
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