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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.


64 × 64 segmented MRI slice of the brain.
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Related In: Results  -  Collection


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fig14: 64 × 64 segmented MRI slice of the brain.

Mentions: Next, we challenge our algorithm with a 64 × 64 image obtainedfrom a segmented MRI slice of the brain. This image was used to generatetotally 6144 (64 positions and 96 angular views) observations. The sinogramdata as well as the data noise (after scaling up) are shown in Figure 11.Notice that we are using the MRI image to generate the PET data, and we are nottrying to solve the MRI tomography problem. The true intensity values are {0, 1, 4}, and the intervals { [0, 0.5], [0.5, 1.5], [3.5, 4.5] } were used forour LSEM algorithm. Compared with Figures 5(c) and 10(c), the inner structureto be recovered here is more complicated, as seen in Figure 14(c). Theevolutions of the ϕ1 and ϕ2 functions areshown in Figures 12 and 13.


Level set method for positron emission tomography.

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

64 × 64 segmented MRI slice of the brain.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig14: 64 × 64 segmented MRI slice of the brain.
Mentions: Next, we challenge our algorithm with a 64 × 64 image obtainedfrom a segmented MRI slice of the brain. This image was used to generatetotally 6144 (64 positions and 96 angular views) observations. The sinogramdata as well as the data noise (after scaling up) are shown in Figure 11.Notice that we are using the MRI image to generate the PET data, and we are nottrying to solve the MRI tomography problem. The true intensity values are {0, 1, 4}, and the intervals { [0, 0.5], [0.5, 1.5], [3.5, 4.5] } were used forour LSEM algorithm. Compared with Figures 5(c) and 10(c), the inner structureto be recovered here is more complicated, as seen in Figure 14(c). Theevolutions of the ϕ1 and ϕ2 functions areshown in Figures 12 and 13.

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.