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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.


Evolution of a two circles using the EM algorithm.
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Related In: Results  -  Collection


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fig3: Evolution of a two circles using the EM algorithm.

Mentions: In our first example we try to reconstruct a 32 × 32 image of twocircles, one inside the other, as seen in Figure 3(e). Total 1536 (32 positionsand 48 angular views) observations were given to us. The sinogram data as wellas the data noise (after scaling up) are shown in Figure 2. The true intensityvalues are {0, 1, 2}. We first test the EM algorithm. After a fewiterations, it is possible to see some inner structures in the PET imagedepicted in Figure 3.


Level set method for positron emission tomography.

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

Evolution of a two circles using the EM algorithm.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig3: Evolution of a two circles using the EM algorithm.
Mentions: In our first example we try to reconstruct a 32 × 32 image of twocircles, one inside the other, as seen in Figure 3(e). Total 1536 (32 positionsand 48 angular views) observations were given to us. The sinogram data as wellas the data noise (after scaling up) are shown in Figure 2. The true intensityvalues are {0, 1, 2}. We first test the EM algorithm. After a fewiterations, it is possible to see some inner structures in the PET imagedepicted in Figure 3.

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.