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Spatial two-tissue compartment model for dynamic contrast-enhanced magnetic resonance imaging.

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Τίτλος: Spatial two-tissue compartment model for dynamic contrast-enhanced magnetic resonance imaging.
Συγγραφείς: Sommer, Julia C.1, Schmid, Volker J.1
Πηγή: Journal of the Royal Statistical Society: Series C (Applied Statistics). Nov2014, Vol. 63 Issue 5, p695-713. 19p.
Θεματικοί όροι: *Bayesian analysis, *Statistical decision making, Oncology education, Regression analysis data processing, Forced vibration (Mechanics), Gaussian Markov random fields, Magnetic resonance imaging equipment
Περίληψη: In the quantitative analysis of dynamic contrast-enhanced magnetic resonance imaging compartment models allow the uptake of contrast medium to be described with biologically meaningful kinetic parameters. As simple models often fail to describe adequately the observed uptake behaviour, more complex compartment models have been proposed. However, the non-linear regression problem arising from more complex compartment models often suffers from parameter redundancy. We incorporate spatial smoothness on the kinetic parameters of a two-tissue compartment model by imposing Gaussian Markov random-field priors on them. We analyse to what extent this spatial regularization helps to avoid parameter redundancy and to obtain stable parameter point estimates per voxel. Choosing a full Bayesian approach, we obtain posteriors and point estimates by running Markov chain Monte Carlo simulations. The approach proposed is evaluated for simulated concentration time curves as well as for in vivo data from a breast cancer study. [ABSTRACT FROM AUTHOR]
Copyright of Journal of the Royal Statistical Society: Series C (Applied Statistics) is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: In the quantitative analysis of dynamic contrast-enhanced magnetic resonance imaging compartment models allow the uptake of contrast medium to be described with biologically meaningful kinetic parameters. As simple models often fail to describe adequately the observed uptake behaviour, more complex compartment models have been proposed. However, the non-linear regression problem arising from more complex compartment models often suffers from parameter redundancy. We incorporate spatial smoothness on the kinetic parameters of a two-tissue compartment model by imposing Gaussian Markov random-field priors on them. We analyse to what extent this spatial regularization helps to avoid parameter redundancy and to obtain stable parameter point estimates per voxel. Choosing a full Bayesian approach, we obtain posteriors and point estimates by running Markov chain Monte Carlo simulations. The approach proposed is evaluated for simulated concentration time curves as well as for in vivo data from a breast cancer study. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of the Royal Statistical Society: Series C (Applied Statistics) is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1111/rssc.12057
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        Text: English
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      – SubjectFull: Bayesian analysis
        Type: general
      – SubjectFull: Statistical decision making
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      – SubjectFull: Oncology education
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      – SubjectFull: Forced vibration (Mechanics)
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      – SubjectFull: Gaussian Markov random fields
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      – SubjectFull: Magnetic resonance imaging equipment
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              Text: Nov2014
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