Smoothing dynamic positron emission tomography time courses using functional principal components
A functional smoothing approach to the analysis of PET time course data is presented. By borrowing information across space and accounting for this pooling through the use of a nonparametric covariate adjustment, it is possible to smooth the PET time course data thus reducing the noise. A new model...
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| Published in | NeuroImage (Orlando, Fla.) Vol. 47; no. 1; pp. 184 - 193 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
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United States
Elsevier Inc
01.08.2009
Elsevier Limited |
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| Abstract | A functional smoothing approach to the analysis of PET time course data is presented. By borrowing information across space and accounting for this pooling through the use of a nonparametric covariate adjustment, it is possible to smooth the PET time course data thus reducing the noise. A new model for functional data analysis, the Multiplicative Nonparametric Random Effects Model, is introduced to more accurately account for the variation in the data. A locally adaptive bandwidth choice helps to determine the correct amount of smoothing at each time point. This preprocessing step to smooth the data then allows subsequent analysis by methods such as Spectral Analysis to be substantially improved in terms of their mean squared error. |
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| AbstractList | A functional smoothing approach to the analysis of PET time course data is presented. By borrowing information across space and accounting for this pooling through the use of a non-parametric covariate adjustment, it is possible to smooth the PET time course data thus reducing the noise. A new model for functional data analysis, the Multiplicative Nonparametric Random Effects Model, is introduced to more accurately account for the variation in the data. A locally adaptive bandwidth choice helps to determine the correct amount of smoothing at each time point. This preprocessing step to smooth the data then allows subsequent analysis by methods such as Spectral Analysis to be substantially improved in terms of their mean squared error. A functional smoothing approach to the analysis of PET time course data is presented. By borrowing information across space and accounting for this pooling through the use of a nonparametric covariate adjustment, it is possible to smooth the PET time course data thus reducing the noise. A new model for functional data analysis, the Multiplicative Nonparametric Random Effects Model, is introduced to more accurately account for the variation in the data. A locally adaptive bandwidth choice helps to determine the correct amount of smoothing at each time point. This preprocessing step to smooth the data then allows subsequent analysis by methods such as Spectral Analysis to be substantially improved in terms of their mean squared error. |
| Author | Wang, Jane-Ling Aston, John A.D. Jiang, Ci-Ren |
| AuthorAffiliation | Department of Statistics, University of California, Davis, CA 95616, U.S.A, wang@wald.ucdavis.edu Institute of Statistical Science, Academia Sinica, Taipei, Taiwan, jaston@stat.sinica.edu.tw CRiSM, Department of Statistics, University of Warwick, CV4 7AL, UK, j.a.d.aston@warwick.ac.uk Department of Statistics, University of California, Davis, CA 95616, U.S.A, crjiang@wald.ucdavis.edu |
| AuthorAffiliation_xml | – name: CRiSM, Department of Statistics, University of Warwick, CV4 7AL, UK, j.a.d.aston@warwick.ac.uk – name: Department of Statistics, University of California, Davis, CA 95616, U.S.A, wang@wald.ucdavis.edu – name: Department of Statistics, University of California, Davis, CA 95616, U.S.A, crjiang@wald.ucdavis.edu – name: Institute of Statistical Science, Academia Sinica, Taipei, Taiwan, jaston@stat.sinica.edu.tw |
| Author_xml | – sequence: 1 givenname: Ci-Ren surname: Jiang fullname: Jiang, Ci-Ren email: crjiang@wald.ucdavis.edu organization: Department of Statistics, University of California, Davis, CA 95616, USA – sequence: 2 givenname: John A.D. surname: Aston fullname: Aston, John A.D. email: j.a.d.aston@warwick.ac.uk, jaston@stat.sinica.edu.tw organization: CRiSM, Department of Statistics, University of Warwick, CV4 7AL, UK – sequence: 3 givenname: Jane-Ling surname: Wang fullname: Wang, Jane-Ling email: wang@wald.ucdavis.edu organization: Department of Statistics, University of California, Davis, CA 95616, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/19344774$$D View this record in MEDLINE/PubMed |
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| Keywords | Neuroreceptor ligands Nonparametric smoothing Functional data analysis Principal component analysis |
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| SubjectTerms | Advantages Algorithms Brain - physiology Carbon Radioisotopes Computer Simulation Diprenorphine Economic models Functional data analysis Humans Mathematical functions Medical imaging Methods Models, Biological Neuroreceptor ligands Noise Nonparametric smoothing Parameter estimation Phantoms, Imaging Positron-Emission Tomography - methods Principal Component Analysis Principal components analysis Signal Processing, Computer-Assisted Statistics, Nonparametric Studies Time Factors |
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| Title | Smoothing dynamic positron emission tomography time courses using functional principal components |
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