Quantitative Analysis of Dynamic Contrast-Enhanced and Diffusion-Weighted Magnetic Resonance Imaging for Oncology in R
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. E...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Foundation for Open Access Statistics
2011-10-01
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| Series: | Journal of Statistical Software |
| Subjects: | |
| Online Access: | http://www.jstatsoft.org/v44/i05/paper |
| _version_ | 1818494458967097344 |
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| author | Volker J. Schmid Brandon Whitcher |
| author_facet | Volker J. Schmid Brandon Whitcher |
| author_sort | Volker J. Schmid |
| collection | DOAJ |
| 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-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. |
| first_indexed | 2024-12-10T18:06:41Z |
| format | Article |
| id | doaj.art-e56e72a363064a72b45b4253830f692c |
| institution | Directory Open Access Journal |
| issn | 1548-7660 |
| language | English |
| last_indexed | 2024-12-10T18:06:41Z |
| publishDate | 2011-10-01 |
| publisher | Foundation for Open Access Statistics |
| record_format | Article |
| series | Journal of Statistical Software |
| spelling | doaj.art-e56e72a363064a72b45b4253830f692c2022-12-22T01:38:36ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602011-10-014405Quantitative Analysis of Dynamic Contrast-Enhanced and Diffusion-Weighted Magnetic Resonance Imaging for Oncology in RVolker J. SchmidBrandon WhitcherThe 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.http://www.jstatsoft.org/v44/i05/papercontrastdcemriS4diffusiondynamicenhancedimagingmagneticresonance. |
| spellingShingle | Volker J. Schmid Brandon Whitcher Quantitative Analysis of Dynamic Contrast-Enhanced and Diffusion-Weighted Magnetic Resonance Imaging for Oncology in R Journal of Statistical Software contrast dcemriS4 diffusion dynamic enhanced imaging magnetic resonance. |
| title | Quantitative Analysis of Dynamic Contrast-Enhanced and Diffusion-Weighted Magnetic Resonance Imaging for Oncology in R |
| title_full | Quantitative Analysis of Dynamic Contrast-Enhanced and Diffusion-Weighted Magnetic Resonance Imaging for Oncology in R |
| title_fullStr | Quantitative Analysis of Dynamic Contrast-Enhanced and Diffusion-Weighted Magnetic Resonance Imaging for Oncology in R |
| title_full_unstemmed | Quantitative Analysis of Dynamic Contrast-Enhanced and Diffusion-Weighted Magnetic Resonance Imaging for Oncology in R |
| title_short | Quantitative Analysis of Dynamic Contrast-Enhanced and Diffusion-Weighted Magnetic Resonance Imaging for Oncology in R |
| title_sort | quantitative analysis of dynamic contrast enhanced and diffusion weighted magnetic resonance imaging for oncology in r |
| topic | contrast dcemriS4 diffusion dynamic enhanced imaging magnetic resonance. |
| url | http://www.jstatsoft.org/v44/i05/paper |
| work_keys_str_mv | AT volkerjschmid quantitativeanalysisofdynamiccontrastenhancedanddiffusionweightedmagneticresonanceimagingforoncologyinr AT brandonwhitcher quantitativeanalysisofdynamiccontrastenhancedanddiffusionweightedmagneticresonanceimagingforoncologyinr |