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Title:

Bayesian Age-Period-Cohort Modeling and Prediction - BAMP

Description:

The software package BAMP provides a method of analyzing incidence or mortality data on the Lexis diagram, using a Bayesian version of an age-period-cohort model. A hierarchical model is assumed with a binomial model in the first-stage. As smoothing priors for the age, period and cohort parameters random walks of first and second order, with and...

The software package BAMP provides a method of analyzing incidence or mortality data on the Lexis diagram, using a Bayesian version of an age-period-cohort model. A hierarchical model is assumed with a binomial model in the first-stage. As smoothing priors for the age, period and cohort parameters random walks of first and second order, with and without an additional unstructured component are available. Unstructured heterogeneity can also be included in the model. In order to evaluate the model fit, posterior deviance, DIC and predictive deviances are computed. By projecting the random walk prior into the future, future death rates can be predicted. Minimize

Publisher:

University of California at Los Angeles, Department of Statistics

Year of Publication:

2007-10-01T00:00:00Z

Document Type:

article

Language:

English

Subjects:

Bayesian hierarchical models ; age-period-cohort models ; prediction ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Scien...

Bayesian hierarchical models ; age-period-cohort models ; prediction ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H Minimize

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Title:

Quantitative Analysis of Dynamic Contrast-Enhanced and Diffusion-Weighted Magnetic Resonance Imaging for Oncology in R

Description:

The package dcemriS4 provides a complete set of data analysis tools for quantitative assessment of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Image processing is provided for the ANALYZE and NIfTI data formats as input with all parameter estimates being output in NIfTI format. Estimation of T1 relaxation from multiple flip-a...

The package dcemriS4 provides a complete set of data analysis tools for quantitative assessment of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Image processing is provided for the ANALYZE and NIfTI data formats as input with all parameter estimates being output in NIfTI format. Estimation of T1 relaxation from multiple flip-angle acquisitions, using either constant or spatially-varying flip angles, is performed via nonlinear regression. Both literature-based and data-driven arterial input functions are available and may be combined with a variety of compartmental models. Kinetic parameters are obtained from nonlinear regression, Bayesian estimation via Markov chain Monte Carlo or Bayesian maximum a posteriori estimation. A non-parametric model, using penalized splines, is also available to characterize the contrast agent concentration time curves. Estimation of the apparent diffusion coefficient (ADC) is provided for diffusion-weighted imaging. Given the size of multi-dimensional data sets commonly acquired in imaging studies, care has been taken to maximize computational efficiency and minimize memory usage. All methods are illustrated using both simulated and real-world medical imaging data available in the public domain. Minimize

Publisher:

University of California, Los Angeles

Year of Publication:

2011-10-01T00:00:00Z

Source:

Journal of Statistical Software, Vol 44, Iss 05 (2011)

Journal of Statistical Software, Vol 44, Iss 05 (2011) Minimize

Document Type:

article

Language:

English

Subjects:

contrast ; dcemriS4 ; diffusion ; dynamic ; enhanced ; imaging ; magnetic ; resonance. ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ...

contrast ; dcemriS4 ; diffusion ; dynamic ; enhanced ; imaging ; magnetic ; resonance. ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H Minimize

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310 Collections of general statistics *(computed)*

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Title:

Bayesian Age-Period-Cohort Modeling and Prediction - BAMP

Description:

The software package BAMP provides a method of analyzing incidence or mortality data on the Lexis diagram, using a Bayesian version of an age-period-cohort model. A hierarchical model is assumed with a binomial model in the first-stage. As smoothing priors for the age, period and cohort parameters random walks of first and second order, with and...

The software package BAMP provides a method of analyzing incidence or mortality data on the Lexis diagram, using a Bayesian version of an age-period-cohort model. A hierarchical model is assumed with a binomial model in the first-stage. As smoothing priors for the age, period and cohort parameters random walks of first and second order, with and without an additional unstructured component are available. Unstructured heterogeneity can also be included in the model. In order to evaluate the model fit, posterior deviance, DIC and predictive deviances are computed. By projecting the random walk prior into the future, future death rates can be predicted. Minimize

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Title:

Working with the DICOM and NIfTI Data Standards in R

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Two packages, oro.dicom and oro.nifti, are provided for the interaction with and manipulation of medical imaging data that conform to the DICOM standard or ANALYZE/NIfTI formats. DICOM data, from a single file or directory tree, may be uploaded into R using basic data structures: a data frame for the header information and a matrix for the image...

Two packages, oro.dicom and oro.nifti, are provided for the interaction with and manipulation of medical imaging data that conform to the DICOM standard or ANALYZE/NIfTI formats. DICOM data, from a single file or directory tree, may be uploaded into R using basic data structures: a data frame for the header information and a matrix for the image data. A list structure is used to organize multiple DICOM files. The S4 class framework is used to develop basic ANALYZE and NIfTI classes, where NIfTI extensions may be used to extend the fixed-byte NIfTI header. One example of this, that has been implemented, is an XML-based audit trail tracking the history of operations applied to a data set. The conversion from DICOM to ANALYZE/NIfTI is straightforward using the capabilities of both packages. The S4 classes have been developed to provide a userfriendly interface to the ANALYZE/NIfTI data formats; allowing easy data input, data output, image processing and visualization. Minimize

Publisher:

University of California, Los Angeles

Year of Publication:

2011-10-01T00:00:00Z

Source:

Journal of Statistical Software, Vol 44, Iss 06 (2011)

Journal of Statistical Software, Vol 44, Iss 06 (2011) Minimize

Document Type:

article

Language:

English

Subjects:

export ; imaging ; import ; medical ; visualization. ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; LC...

export ; imaging ; import ; medical ; visualization. ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H Minimize

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310 Collections of general statistics *(computed)*

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Title:

dcemriS4: A Package for Medical Image Analysis

Description:

Quantitative analysis of perfusion imaging using dynamic contrast-enhanced MRI (DCE-MRI) is achieved through a series of processing steps, starting with the raw data acquired from the MRI scanner, and involves a combination of physics, mathematics, engineering and statistics. The purpose of the dcemriS4 package is to provide a collection of func...

Quantitative analysis of perfusion imaging using dynamic contrast-enhanced MRI (DCE-MRI) is achieved through a series of processing steps, starting with the raw data acquired from the MRI scanner, and involves a combination of physics, mathematics, engineering and statistics. The purpose of the dcemriS4 package is to provide a collection of functions that move the Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2011-06-26

Source:

http://cran.at.r-project.org/web/packages/dcemriS4/vignettes/dcemriS4.pdf

http://cran.at.r-project.org/web/packages/dcemriS4/vignettes/dcemriS4.pdf Minimize

Document Type:

text

Language:

en

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Title:

Working with the DICOM Data Standard in R Brandon Whitcher Pfizer Worldwide R&D

Description:

The package oro.dicom facilitates the interaction with and manipulation of medical imaging data that conform to the DICOM standard. DICOM data, from a single file or single directory or directory tree, may be uploaded into R using basic data structures: a data frame for the header information and a matrix for the image data. A list structure is ...

The package oro.dicom facilitates the interaction with and manipulation of medical imaging data that conform to the DICOM standard. DICOM data, from a single file or single directory or directory tree, may be uploaded into R using basic data structures: a data frame for the header information and a matrix for the image data. A list structure is used to organize multiple DICOM files. The conversion from DICOM to ANALYZE/NIfTI is straightforward using the capabilities of oro.dicom and oro.nifti. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2014-12-04

Source:

http://cran.r-project.org/web/packages/oro.dicom/vignettes/dicom.pdf

http://cran.r-project.org/web/packages/oro.dicom/vignettes/dicom.pdf Minimize

Document Type:

text

Language:

en

Subjects:

export ; imaging ; import ; medical ; visualization

export ; imaging ; import ; medical ; visualization Minimize

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Title:

Working with the DICOM Data Standard in R Brandon Whitcher Mango Solutions

Author:

Description:

The package oro.dicom facilitates the interaction with and manipulation of medical imaging data that conform to the DICOM standard. DICOM data, from a single file or single directory or directory tree, may be uploaded into R using basic data structures: a data frame for the header information and a matrix for the image data. A list structure is ...

The package oro.dicom facilitates the interaction with and manipulation of medical imaging data that conform to the DICOM standard. DICOM data, from a single file or single directory or directory tree, may be uploaded into R using basic data structures: a data frame for the header information and a matrix for the image data. A list structure is used to organize multiple DICOM files. The conversion from DICOM to ANALYZE/NIfTI is straightforward using the capabilities of oro.dicom and oro.nifti. Keywords:˜export, imaging, import, medical, visualization. 1. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2013-07-24

Source:

http://cran.at.r-project.org/web/packages/oro.dicom/vignettes/dicom.pdf

http://cran.at.r-project.org/web/packages/oro.dicom/vignettes/dicom.pdf Minimize

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Language:

en

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Title:

resonance

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Description:

A Bayesian framework for pharmacokinetic modelling in dynamic contrast-enhanced magnetic

A Bayesian framework for pharmacokinetic modelling in dynamic contrast-enhanced magnetic Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2008-07-01

Source:

http://www.maths.leeds.ac.uk/statistics/workshop/lasr2006/proceedings/schmid2.pdf

http://www.maths.leeds.ac.uk/statistics/workshop/lasr2006/proceedings/schmid2.pdf Minimize

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text

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en

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Title:

ABSTRACT Attenuation resilient AIF estimation based on hierarchical

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Bayesian modelling for first pass myocardial perfusion MRI Non-linear attenuation of the Arterial Input Function (AIF) is a major problem in first-pass MR perfusion imaging due to the high concentration of the contrast agent in the blood pool. This paper presents a technique to reconstruct the true AIF using signal intensities in the myocardium ...

Bayesian modelling for first pass myocardial perfusion MRI Non-linear attenuation of the Arterial Input Function (AIF) is a major problem in first-pass MR perfusion imaging due to the high concentration of the contrast agent in the blood pool. This paper presents a technique to reconstruct the true AIF using signal intensities in the myocardium and the attenuated AIF based on a Hierarchical Bayesian Model (HBM). With the proposed method, both the AIF and the response function are modeled as smoothed functions by using Bayesian penalty splines (P-Splines). The derived AIF is then used to estimate the impulse response of the myocardium based on deconvolution analysis. The proposed technique is validated both with simulated data using the MMID4 model and ten in vivo data sets for estimating myocardial perfusion reserve rates. The results demonstrate the ability of the proposed technique in accurately reconstructing the desired AIF for myocardial perfusion quantification. The method does not involve any MRI pulse sequence modification, and thus is expected to have wider clinical impact. HIERARCHICAL BAYESIAN MODEL We propose a Hierarchical Bayesian Model (HBM) for the reconstruction of the AIF, where both the AIF and the response function are modelled as smoothed functions by using Bayesian penalty splines (P-Splines) [2]. Since the information from the myocardium is sparse, a relatively informative prior model is used for the response function. Subsequently, the derived AIF can be used in existing myocardial impulse response estimation techniques based on deconvolution analysis Y signal in myocardial tissue S true contrast conc. intissue f response in myoc. tissue β spline parameters φ smoothness parameters Z signal in LV blood pool A true AIF γ spline parameters ψ smoothness parameters Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2008-07-01

Source:

http://pangast.de/volkerschmid/media/directory/uploads/6c8349cc7260ae62e3b1396831a8398f.pdf

http://pangast.de/volkerschmid/media/directory/uploads/6c8349cc7260ae62e3b1396831a8398f.pdf Minimize

Document Type:

text

Language:

en

DDC:

310 Collections of general statistics *(computed)*

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

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Title:

Working with the NIfTI Data Standard in R Brandon Whitcher Mango Solutions

Author:

Description:

The package oro.nifti facilitates the interaction with and manipulation of medical imaging data that conform to the ANALYZE, NIfTI and AFNI formats. The S4 class framework is used to develop basic ANALYZE and NIfTI classes, where NIfTI extensions may be used to extend the fixed-byte NIfTI header. One example of this, that has been implemented, i...

The package oro.nifti facilitates the interaction with and manipulation of medical imaging data that conform to the ANALYZE, NIfTI and AFNI formats. The S4 class framework is used to develop basic ANALYZE and NIfTI classes, where NIfTI extensions may be used to extend the fixed-byte NIfTI header. One example of this, that has been implemented, is an XML-based “audit trail ” tracking the history of operations applied to a data set. The conversion from DICOM to ANALYZE/NIfTI is straightforward using the capabilities of oro.dicom. The S4 classes have been developed to provide a user-friendly interface to the ANALYZE/NIfTI data formats; allowing easy data input, data output, image processing and visualization. Keywords:˜export, imaging, import, medical, visualization. 1. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2013-07-24

Source:

http://cran.r-project.org/web/packages/oro.nifti/vignettes/nifti.pdf

http://cran.r-project.org/web/packages/oro.nifti/vignettes/nifti.pdf Minimize

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text

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en

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