FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2201624946> ?p ?o ?g. }

• W2201624946 endingPage "41" @default.
• W2201624946 startingPage "31" @default.
• W2201624946 abstract "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.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.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.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.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." @default.
• W2201624946 created "2016-06-24" @default.
• W2201624946 creator A5000960000 @default.
• W2201624946 creator A5005283230 @default.
• W2201624946 creator A5049568556 @default.
• W2201624946 creator A5069072700 @default.
• W2201624946 date "2016-01-01" @default.
• W2201624946 modified "2023-10-14" @default.
• W2201624946 title "Boosting in Nonlinear Regression Models with an Application to DCE-MRI Data" @default.
• W2201624946 cites W117116391 @default.
• W2201624946 cites W1505178423 @default.
• W2201624946 cites W1678356000 @default.
• W2201624946 cites W1917918477 @default.
• W2201624946 cites W1963913232 @default.
• W2201624946 cites W1973217014 @default.
• W2201624946 cites W1985505753 @default.
• W2201624946 cites W1990990856 @default.
• W2201624946 cites W1992549534 @default.
• W2201624946 cites W2011446064 @default.
• W2201624946 cites W2024046085 @default.
• W2201624946 cites W2042063151 @default.
• W2201624946 cites W2042098439 @default.
• W2201624946 cites W2070772485 @default.
• W2201624946 cites W2071721660 @default.
• W2201624946 cites W2072626310 @default.
• W2201624946 cites W2088883866 @default.
• W2201624946 cites W2094209066 @default.
• W2201624946 cites W2099303263 @default.
• W2201624946 cites W2104311705 @default.
• W2201624946 cites W2104781626 @default.
• W2201624946 cites W2108610949 @default.
• W2201624946 cites W2112284512 @default.
• W2201624946 cites W2114165294 @default.
• W2201624946 cites W2122825543 @default.
• W2201624946 cites W2125271071 @default.
• W2201624946 cites W2140443056 @default.
• W2201624946 cites W2150390194 @default.
• W2201624946 cites W2155751421 @default.
• W2201624946 cites W2162363205 @default.
• W2201624946 cites W2489605225 @default.
• W2201624946 cites W2787894218 @default.
• W2201624946 cites W2952563653 @default.
• W2201624946 cites W3099723433 @default.
• W2201624946 cites W3105340263 @default.
• W2201624946 cites W347231809 @default.
• W2201624946 cites W4292334794 @default.
• W2201624946 doi "https://doi.org/10.3414/me14-01-0131" @default.
• W2201624946 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/26577400" @default.
• W2201624946 hasPublicationYear "2016" @default.
• W2201624946 type Work @default.
• W2201624946 sameAs 2201624946 @default.
• W2201624946 citedByCount "3" @default.
• W2201624946 countsByYear W22016249462017 @default.
• W2201624946 countsByYear W22016249462019 @default.
• W2201624946 countsByYear W22016249462023 @default.
• W2201624946 crossrefType "journal-article" @default.
• W2201624946 hasAuthorship W2201624946A5000960000 @default.
• W2201624946 hasAuthorship W2201624946A5005283230 @default.
• W2201624946 hasAuthorship W2201624946A5049568556 @default.
• W2201624946 hasAuthorship W2201624946A5069072700 @default.
• W2201624946 hasConcept C105795698 @default.
• W2201624946 hasConcept C111368507 @default.
• W2201624946 hasConcept C119857082 @default.
• W2201624946 hasConcept C121332964 @default.
• W2201624946 hasConcept C126838900 @default.
• W2201624946 hasConcept C127313418 @default.
• W2201624946 hasConcept C143409427 @default.
• W2201624946 hasConcept C152877465 @default.
• W2201624946 hasConcept C153180895 @default.
• W2201624946 hasConcept C154945302 @default.
• W2201624946 hasConcept C158622935 @default.
• W2201624946 hasConcept C203635412 @default.
• W2201624946 hasConcept C2776502983 @default.
• W2201624946 hasConcept C33923547 @default.
• W2201624946 hasConcept C41008148 @default.
• W2201624946 hasConcept C41727105 @default.
• W2201624946 hasConcept C46686674 @default.
• W2201624946 hasConcept C46889948 @default.
• W2201624946 hasConcept C54170458 @default.
• W2201624946 hasConcept C62520636 @default.
• W2201624946 hasConcept C71924100 @default.
• W2201624946 hasConcept C83546350 @default.
• W2201624946 hasConceptScore W2201624946C105795698 @default.
• W2201624946 hasConceptScore W2201624946C111368507 @default.
• W2201624946 hasConceptScore W2201624946C119857082 @default.
• W2201624946 hasConceptScore W2201624946C121332964 @default.
• W2201624946 hasConceptScore W2201624946C126838900 @default.
• W2201624946 hasConceptScore W2201624946C127313418 @default.
• W2201624946 hasConceptScore W2201624946C143409427 @default.
• W2201624946 hasConceptScore W2201624946C152877465 @default.
• W2201624946 hasConceptScore W2201624946C153180895 @default.
• W2201624946 hasConceptScore W2201624946C154945302 @default.
• W2201624946 hasConceptScore W2201624946C158622935 @default.
• W2201624946 hasConceptScore W2201624946C203635412 @default.
• W2201624946 hasConceptScore W2201624946C2776502983 @default.
• W2201624946 hasConceptScore W2201624946C33923547 @default.
• W2201624946 hasConceptScore W2201624946C41008148 @default.
• W2201624946 hasConceptScore W2201624946C41727105 @default.

• next