Premium
Lesion quantification in oncological positron emission tomography: A maximum likelihood partial volume correction strategy
Author(s) -
De Bernardi Elisabetta,
Faggiano Elena,
Zito Felicia,
Gerundini Paolo,
Baselli Giuseppe
Publication year - 2009
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 - 10.1118/1.3130019
Subject(s) - partial volume , iterative reconstruction , imaging phantom , positron emission tomography , volume (thermodynamics) , algorithm , basis function , point spread function , expectation–maximization algorithm , segmentation , tomography , iterated function , image segmentation , computer science , correction for attenuation , mathematics , nuclear medicine , artificial intelligence , physics , optics , maximum likelihood , statistics , mathematical analysis , medicine , quantum mechanics
A maximum likelihood (ML) partial volume effect correction (PVEC) strategy for the quantification of uptake and volume of oncological lesions inF18‐FDG positron emission tomography is proposed. The algorithm is based on the application of ML reconstruction on volumetric regional basis functions initially defined on a smooth standard clinical image and iteratively updated in terms of their activity and volume. The volume of interest (VOI) containing a previously detected region is segmented by a k ‐means algorithm in three regions: A central region surrounded by a partial volume region and a spill‐out region. All volume outside the VOI (background with all other structures) is handled as a unique basis function and therefore “frozen” in the reconstruction process except for a gain coefficient. The coefficients of the regional basis functions are iteratively estimated with an attenuation‐weighted ordered subset expectation maximization (AWOSEM) algorithm in which a 3D, anisotropic, space variant model of point spread function (PSF) is included for resolution recovery. The reconstruction‐segmentation process is iterated until convergence; at each iteration, segmentation is performed on the reconstructed image blurred by the system PSF in order to update the partial volume and spill‐out regions. The developed PVEC strategy was tested on sphere phantom studies with activity contrasts of 7.5 and 4 and compared to a conventional recovery coefficient method. Improved volume and activity estimates were obtained with low computational costs, thanks to blur recovery and to a better local approximation to ML convergence.