Abstract:
BACKGROUND:For the statistical analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data, compartment models are a commonly used tool. By these models, the observed uptake of contrast agent in some tissue over time is linked to physiologic properties like capillary permeability and blood flow. Up to now, models of different complexity have been used, and it is still unclear which model should be used in which situation. In previous studies, it has been found that for DCE-MRI data, the number of compartments differs for different types of tissue, and that in cancerous tissue, it might actually differ over a region of voxels of one DCE-MR image. OBJECTIVES:To find the appropriate number of compartments and estimate the parameters of a regression model for each voxel in an DCE-MR image. With that, tumors in an DCE-MR image can be located, and for example therapy success can be assessed. METHODS:The observed uptake of contrast agent in a voxel of an image of some tissue is described by a concentration time curve. This curve can be modeled using a nonlinear regression model. We present a boosting approach with nonlinear regression as base procedure, which allows us to estimate the number of compartments and the related parameters for each voxel of an DCE-MR image. In addition, a spatially regularized version of this approach is proposed. RESULTS:With the proposed approach, the number of compartments - and with that the complexity of the model - per voxel is not fixed but data-driven, which allows us to fit models of adequate complexity to the concentration time curves of all voxels. The parameters of the model remain nevertheless interpretable because of the underlying compartment model. CONCLUSIONS:The proposed boosting approaches outperform all competing methods considered in this paper regarding the correct localization of tumors in DCE-MR images as well as the spatial homogeneity of the estimated number of compartments across the image, and the definition of the tumor edge.
journal_name
Methods Inf Medjournal_title
Methods of information in medicineauthors
Feilke M,Bischl B,Schmid VJ,Gertheiss Jdoi
10.3414/ME14-01-0131subject
Has Abstractpub_date
2016-01-01 00:00:00pages
31-41issue
1eissn
0026-1270issn
2511-705Xpii
14-01-0131journal_volume
55pub_type
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journal_title:Methods of information in medicine
pub_type: 杂志文章
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abstract::Artificial neural networks (ANNs) are compared to standard statistical methods for outcome prediction in biomedical problems. A general method for using genetic algorithms to "evolve" ANN architecture (EANN) is presented. Accuracy of logistic regression, a fully interconnected ANN, and an EANN for predicting depressio...
journal_title:Methods of information in medicine
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journal_title:Methods of information in medicine
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journal_title:Methods of information in medicine
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journal_title:Methods of information in medicine
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journal_title:Methods of information in medicine
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journal_title:Methods of information in medicine
pub_type: 杂志文章
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journal_title:Methods of information in medicine
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journal_title:Methods of information in medicine
pub_type: 临床试验,杂志文章
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journal_title:Methods of information in medicine
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journal_title:Methods of information in medicine
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doi:
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journal_title:Methods of information in medicine
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doi:
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journal_title:Methods of information in medicine
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journal_title:Methods of information in medicine
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doi:
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journal_title:Methods of information in medicine
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journal_title:Methods of information in medicine
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doi:
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journal_title:Methods of information in medicine
pub_type: 杂志文章
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journal_title:Methods of information in medicine
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journal_title:Methods of information in medicine
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journal_title:Methods of information in medicine
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doi:
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