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Level set method for positron emission tomography.

Chan TF, Li H, Lysaker M, Tai XC - Int J Biomed Imaging (2007)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, University of California, Los Angeles, 405 Hilgard Avenue, Los Angeles, CA 90095-1555, USA.

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.

No MeSH data available.


Construction of a PET image with different algorithms.
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig10: Construction of a PET image with different algorithms.

Mentions: In less than 600 iterations, the two level setfunctions have converged. Combining ϕ1 from Figure 8(c) and ϕ2 from Figure 9(c) together with (16), we get the PET image depicted in Figure 10(c). Inthis test, we used c4 (background), c2 (gray matter),and c1 = c3 (white matter).The intervals for the intensity values are: { [0, 0.5], [0.5, 1.5], [3.5, 4.5] }. After 600 iterations, the intensity values arerecovered pretty well as { 0, 0.9802, 4.0620 }.


Level set method for positron emission tomography.

Chan TF, Li H, Lysaker M, Tai XC - Int J Biomed Imaging (2007)

Construction of a PET image with different algorithms.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2266822&req=5

fig10: Construction of a PET image with different algorithms.
Mentions: In less than 600 iterations, the two level setfunctions have converged. Combining ϕ1 from Figure 8(c) and ϕ2 from Figure 9(c) together with (16), we get the PET image depicted in Figure 10(c). Inthis test, we used c4 (background), c2 (gray matter),and c1 = c3 (white matter).The intervals for the intensity values are: { [0, 0.5], [0.5, 1.5], [3.5, 4.5] }. After 600 iterations, the intensity values arerecovered pretty well as { 0, 0.9802, 4.0620 }.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, University of California, Los Angeles, 405 Hilgard Avenue, Los Angeles, CA 90095-1555, USA.

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.

No MeSH data available.