Document Detail


Semi-parametric analysis of dynamic contrast-enhanced MRI using Bayesian P-splines.
MedLine Citation:
PMID:  17354949     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Current approaches to quantitative analysis of DCE-MRI with non-linear models involve the convolution of an arterial input function (AIF) with the contrast agent concentration at a voxel or regional level. Full quantification provides meaningful biological parameters but is complicated by the issues related to convergence, (de-)convolution of the AIF, and goodness of fit. To overcome these problems, this paper presents a penalized spline smoothing approach to model the data in a semi-parametric way. With this method, the AIF is convolved with a set of B-splines to produce the design matrix, and modeling of the resulting deconvolved biological parameters is obtained in a way that is similar to the parametric models. Further kinetic parameters are obtained by fitting a non-linear model to the estimated response function and detailed validation of the method, both with simulated and in vivo data is
Authors:
Volker J Schmid; Brandon Whitcher; Guang-Zhong Yang
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Publication Detail:
Type:  Evaluation Studies; Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention     Volume:  9     ISSN:  -     ISO Abbreviation:  Med Image Comput Comput Assist Interv     Publication Date:  2006  
Date Detail:
Created Date:  2007-03-14     Completed Date:  2007-04-06     Revised Date:  2009-12-11    
Medline Journal Info:
Nlm Unique ID:  101249582     Medline TA:  Med Image Comput Comput Assist Interv     Country:  Germany    
Other Details:
Languages:  eng     Pagination:  679-86     Citation Subset:  IM    
Affiliation:
Institute for Biomedical Engineering, Imperial College, South Kensington, London SW7 2AZ, United Kingdom. v.schmid@imperial.ac.uk
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Artificial Intelligence*
Bayes Theorem
Breast Neoplasms / pathology*
Contrast Media
Gadolinium DTPA / diagnostic use*
Humans
Image Enhancement / methods*
Image Interpretation, Computer-Assisted / methods*
Magnetic Resonance Imaging / methods*
Models, Biological
Models, Statistical
Numerical Analysis, Computer-Assisted
Pattern Recognition, Automated / methods*
Reproducibility of Results
Sensitivity and Specificity
Chemical
Reg. No./Substance:
0/Contrast Media; 80529-93-7/Gadolinium DTPA

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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