Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Bayesian spatial temporal mixtures approach to kinetic parametric
  images in dynamic Positron Emission Tomography   We present a fully Bayesian statistical approach to the problem of
compartmental modelling in the context of Positron Emission Tomography. We
cluster homogeneous region of interest and perform kinetic parameter estimation
simultaneously. A mixture modelling approach is adopted, incorporating both
spatial and temporal information based on reconstructed dynamic PET image. Our
modelling approach is flexible, and provides uncertainty estimates for the
estimated kinetic parameters. Crucially, the proposed method allows us to
determine the unknown number of clusters, which has a great impact on resulting
estimated kinetic parameters. We demonstrate our method on simulated dynamic
Myocardial PET data, and show that our method is superior to standard
curve-fitting approach.

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The categories of this paper are: math
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The categories of this paper are: solv-int
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The categories of this paper are: physics
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
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The categories of this paper are: q-fin
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The categories of this paper are: econ
27
The categories of this paper are: comp-gas
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The categories of this paper are: acc-phys
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The categories of this paper are: funct-an
25
The categories of this paper are: patt-sol
35
The categories of this paper are: mtrl-th