Year: 2007
Type: article
Abstract: In positron emission tomography (PET), a radioactive compound is injected into the body to promote a tissue-dependent emission rate. Expectation maximization (EM) reconstruction algorithms are iterative techniques which estimate the concentration coefficients that provide the best fitted solution, for example, a maximum likelihood estimate. In this paper, we combine the EM algorithm with a level set approach. The level set method is used to capture the coarse scale information and the discontinuities of the concentration coefficients. An intrinsic advantage of the level set formulation is that anatomical information can be efficiently incorporated and used in an easy and natural way. We utilize a multiple level set formulation to represent the geometry of the objects in the scene. The proposed algorithm can be applied to any PET configuration, without major modifications.
DOI: 10.1155/2007/26950
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,000
Awards: G1055581497, G1127049006, G1954667523, G2763439099, G434512558, G4450509467, G4752598178, U.S.- France Cooperative Research: Multiresolution and Multiscale Algorithms on Unstructured Meshes for Computational Sciences, Center for Computational Biology (CCB)(RMI), G5273507668, New Models and Fast Algorithms for Variational PDE Image Processing, G5486160198, G597511952, G6559869815, G6850849438, G7698688452, Computional Brain Biology from Genotype to Phenotype, G7825351146, Scalable Multilevel Algorithms in Computational Sciences, G8550742248, G8742363288, G8866997533, G8876996369, G8893466400, G960723348
OpenAlex ID: W1996360227