Spect image reconstruction using compound prior models

Antonio López; Rafael Molina; Javier Mateos; Aggelos K. Katsaggelos

doi:10.1142/S0218001402001708

Spect image reconstruction using compound prior models

Antonio López^*, Rafael Molina, Javier Mateos, Aggelos K. Katsaggelos

^*Corresponding author for this work

MRC - Materials Research Center

Research output: Contribution to journal › Article › peer-review

6 Scopus citations

Abstract

We propose a new iterative method for Maximum a Posteriori (MAP) reconstruction of SPECT (Single Photon Emission Computed Tomography) images. The method uses Compound Gauss Markov Random Fields (CGMRF) as prior model and is stochastic for the line process and deterministic for the reconstruction. Synthetic and real images are used to compare the new method with existing ones.

Original language	English (US)
Pages (from-to)	317-330
Number of pages	14
Journal	International Journal of Pattern Recognition and Artificial Intelligence
Volume	16
Issue number	3
DOIs	https://doi.org/10.1142/S0218001402001708
State	Published - May 2002

Funding

This work has been partially supported by the \CICYT" (Comision Nacional de Ciencia y Tecnolog a) under contract TIC2000-1275.

Keywords

Bayesian reconstruction
Compound Gauss Markov random fields
Deterministic image reconstruction
SPECT imaging
Simulated annealing

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition
Artificial Intelligence

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10.1142/S0218001402001708

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title = "Spect image reconstruction using compound prior models",

abstract = "We propose a new iterative method for Maximum a Posteriori (MAP) reconstruction of SPECT (Single Photon Emission Computed Tomography) images. The method uses Compound Gauss Markov Random Fields (CGMRF) as prior model and is stochastic for the line process and deterministic for the reconstruction. Synthetic and real images are used to compare the new method with existing ones.",

keywords = "Bayesian reconstruction, Compound Gauss Markov random fields, Deterministic image reconstruction, SPECT imaging, Simulated annealing",

author = "Antonio L{\'o}pez and Rafael Molina and Javier Mateos and Katsaggelos, {Aggelos K.}",

note = "Funding Information: This work has been partially supported by the \CICYT{"} (Comision Nacional de Ciencia y Tecnolog a) under contract TIC2000-1275.",

year = "2002",

month = may,

doi = "10.1142/S0218001402001708",

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TY - JOUR

T1 - Spect image reconstruction using compound prior models

AU - López, Antonio

AU - Molina, Rafael

AU - Mateos, Javier

AU - Katsaggelos, Aggelos K.

N1 - Funding Information: This work has been partially supported by the \CICYT" (Comision Nacional de Ciencia y Tecnolog a) under contract TIC2000-1275.

PY - 2002/5

Y1 - 2002/5

N2 - We propose a new iterative method for Maximum a Posteriori (MAP) reconstruction of SPECT (Single Photon Emission Computed Tomography) images. The method uses Compound Gauss Markov Random Fields (CGMRF) as prior model and is stochastic for the line process and deterministic for the reconstruction. Synthetic and real images are used to compare the new method with existing ones.

AB - We propose a new iterative method for Maximum a Posteriori (MAP) reconstruction of SPECT (Single Photon Emission Computed Tomography) images. The method uses Compound Gauss Markov Random Fields (CGMRF) as prior model and is stochastic for the line process and deterministic for the reconstruction. Synthetic and real images are used to compare the new method with existing ones.

KW - Bayesian reconstruction

KW - Compound Gauss Markov random fields

KW - Deterministic image reconstruction

KW - SPECT imaging

KW - Simulated annealing

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DO - 10.1142/S0218001402001708

M3 - Article

AN - SCOPUS:0036565582

SN - 0218-0014

VL - 16

SP - 317

EP - 330

JO - International Journal of Pattern Recognition and Artificial Intelligence

JF - International Journal of Pattern Recognition and Artificial Intelligence

IS - 3

ER -

Spect image reconstruction using compound prior models

Abstract

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Keywords

ASJC Scopus subject areas

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