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Multiple Importance Sampling for PET
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DOI：10.1109/TMI.2014.2300932.png)
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摘要
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This paper proposes the application of multiple importance sampling in fully 3-D positron emission tomography to speed up the iterative reconstruction process. The proposed method combines the results of lines of responses (LOR) driven and voxel driven projections keeping their advantages, like importance sampling, performance and parallel execution on graphics processing units. Voxel driven methods can focus on point like features while LOR driven approaches are efficient in reconstructing homogeneous regions. The theoretical basis of the combination is the application of the mixture of the samples generated by the individual importance sampling methods, emphasizing a particular method where it is better than others. The proposed algorithms are built into the Tera-tomo system.
Keyword:
Graphics processing units (GPU)
importance sampling
maximum likelihood-expectation maximization (ML-EM) reconstruction
Monte Carlo methods
positron emission tomography (PET)
scatter compensation
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