DCEMRI.jl: a fast, validated, open source toolkit for dynamic contrast enhanced MRI analysis

We present a fast, validated, open-source toolkit for processing dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data. We validate it against the Quantitative Imaging Biomarkers Alliance (QIBA) Standard and Extended Tofts-Kety phantoms and find near perfect recovery in the absence of...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	David S. Smith [verfasserIn] Xia Li [verfasserIn] Lori R. Arlinghaus [verfasserIn] Thomas E. Yankeelov [verfasserIn] E. Brian Welch [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2015

Schlagwörter:	Magnetic resonance imaging DCE Quantitative imaging biomarkers qMRI Cancer Parallel computing

Übergeordnetes Werk:	In: PeerJ - PeerJ Inc., 2013, 3, p e909(2015)
Übergeordnetes Werk:	volume:3, p e909 ; year:2015

Links:	Link aufrufen Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.7717/peerj.909

Katalog-ID:	DOAJ016116941

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allfieldsGer	10.7717/peerj.909 doi (DE-627)DOAJ016116941 (DE-599)DOAJd384be8656464fb6b6b0f0c04780feca DE-627 ger DE-627 rakwb eng QH301-705.5 David S. Smith verfasserin aut DCEMRI.jl: a fast, validated, open source toolkit for dynamic contrast enhanced MRI analysis 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We present a fast, validated, open-source toolkit for processing dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data. We validate it against the Quantitative Imaging Biomarkers Alliance (QIBA) Standard and Extended Tofts-Kety phantoms and find near perfect recovery in the absence of noise, with an estimated 10–20× speedup in run time compared to existing tools. To explain the observed trends in the fitting errors, we present an argument about the conditioning of the Jacobian in the limit of small and large parameter values. We also demonstrate its use on an in vivo data set to measure performance on a realistic application. For a 192 × 192 breast image, we achieved run times of <1 s. Finally, we analyze run times scaling with problem size and find that the run time per voxel scales as O(N1.9), where N is the number of time points in the tissue concentration curve. DCEMRI.jl was much faster than any other analysis package tested and produced comparable accuracy, even in the presence of noise. Magnetic resonance imaging DCE Quantitative imaging biomarkers qMRI Cancer Parallel computing Medicine R Biology (General) Xia Li verfasserin aut Lori R. Arlinghaus verfasserin aut Thomas E. Yankeelov verfasserin aut E. Brian Welch verfasserin aut In PeerJ PeerJ Inc., 2013 3, p e909(2015) (DE-627)736558624 (DE-600)2703241-3 21678359 nnns volume:3, p e909 year:2015 https://doi.org/10.7717/peerj.909 kostenfrei https://doaj.org/article/d384be8656464fb6b6b0f0c04780feca kostenfrei https://peerj.com/articles/909.pdf kostenfrei https://peerj.com/articles/909/ kostenfrei https://doaj.org/toc/2167-8359 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3, p e909 2015
allfieldsSound	10.7717/peerj.909 doi (DE-627)DOAJ016116941 (DE-599)DOAJd384be8656464fb6b6b0f0c04780feca DE-627 ger DE-627 rakwb eng QH301-705.5 David S. Smith verfasserin aut DCEMRI.jl: a fast, validated, open source toolkit for dynamic contrast enhanced MRI analysis 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We present a fast, validated, open-source toolkit for processing dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data. We validate it against the Quantitative Imaging Biomarkers Alliance (QIBA) Standard and Extended Tofts-Kety phantoms and find near perfect recovery in the absence of noise, with an estimated 10–20× speedup in run time compared to existing tools. To explain the observed trends in the fitting errors, we present an argument about the conditioning of the Jacobian in the limit of small and large parameter values. We also demonstrate its use on an in vivo data set to measure performance on a realistic application. For a 192 × 192 breast image, we achieved run times of <1 s. Finally, we analyze run times scaling with problem size and find that the run time per voxel scales as O(N1.9), where N is the number of time points in the tissue concentration curve. DCEMRI.jl was much faster than any other analysis package tested and produced comparable accuracy, even in the presence of noise. Magnetic resonance imaging DCE Quantitative imaging biomarkers qMRI Cancer Parallel computing Medicine R Biology (General) Xia Li verfasserin aut Lori R. Arlinghaus verfasserin aut Thomas E. Yankeelov verfasserin aut E. Brian Welch verfasserin aut In PeerJ PeerJ Inc., 2013 3, p e909(2015) (DE-627)736558624 (DE-600)2703241-3 21678359 nnns volume:3, p e909 year:2015 https://doi.org/10.7717/peerj.909 kostenfrei https://doaj.org/article/d384be8656464fb6b6b0f0c04780feca kostenfrei https://peerj.com/articles/909.pdf kostenfrei https://peerj.com/articles/909/ kostenfrei https://doaj.org/toc/2167-8359 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3, p e909 2015
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callnumber-first	Q - Science
author	David S. Smith
spellingShingle	David S. Smith misc QH301-705.5 misc Magnetic resonance imaging misc DCE misc Quantitative imaging biomarkers misc qMRI misc Cancer misc Parallel computing misc Medicine misc R misc Biology (General) DCEMRI.jl: a fast, validated, open source toolkit for dynamic contrast enhanced MRI analysis
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topic	misc QH301-705.5 misc Magnetic resonance imaging misc DCE misc Quantitative imaging biomarkers misc qMRI misc Cancer misc Parallel computing misc Medicine misc R misc Biology (General)
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title	DCEMRI.jl: a fast, validated, open source toolkit for dynamic contrast enhanced MRI analysis
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title_auth	DCEMRI.jl: a fast, validated, open source toolkit for dynamic contrast enhanced MRI analysis
abstract	We present a fast, validated, open-source toolkit for processing dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data. We validate it against the Quantitative Imaging Biomarkers Alliance (QIBA) Standard and Extended Tofts-Kety phantoms and find near perfect recovery in the absence of noise, with an estimated 10–20× speedup in run time compared to existing tools. To explain the observed trends in the fitting errors, we present an argument about the conditioning of the Jacobian in the limit of small and large parameter values. We also demonstrate its use on an in vivo data set to measure performance on a realistic application. For a 192 × 192 breast image, we achieved run times of <1 s. Finally, we analyze run times scaling with problem size and find that the run time per voxel scales as O(N1.9), where N is the number of time points in the tissue concentration curve. DCEMRI.jl was much faster than any other analysis package tested and produced comparable accuracy, even in the presence of noise.
abstractGer	We present a fast, validated, open-source toolkit for processing dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data. We validate it against the Quantitative Imaging Biomarkers Alliance (QIBA) Standard and Extended Tofts-Kety phantoms and find near perfect recovery in the absence of noise, with an estimated 10–20× speedup in run time compared to existing tools. To explain the observed trends in the fitting errors, we present an argument about the conditioning of the Jacobian in the limit of small and large parameter values. We also demonstrate its use on an in vivo data set to measure performance on a realistic application. For a 192 × 192 breast image, we achieved run times of <1 s. Finally, we analyze run times scaling with problem size and find that the run time per voxel scales as O(N1.9), where N is the number of time points in the tissue concentration curve. DCEMRI.jl was much faster than any other analysis package tested and produced comparable accuracy, even in the presence of noise.
abstract_unstemmed	We present a fast, validated, open-source toolkit for processing dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data. We validate it against the Quantitative Imaging Biomarkers Alliance (QIBA) Standard and Extended Tofts-Kety phantoms and find near perfect recovery in the absence of noise, with an estimated 10–20× speedup in run time compared to existing tools. To explain the observed trends in the fitting errors, we present an argument about the conditioning of the Jacobian in the limit of small and large parameter values. We also demonstrate its use on an in vivo data set to measure performance on a realistic application. For a 192 × 192 breast image, we achieved run times of <1 s. Finally, we analyze run times scaling with problem size and find that the run time per voxel scales as O(N1.9), where N is the number of time points in the tissue concentration curve. DCEMRI.jl was much faster than any other analysis package tested and produced comparable accuracy, even in the presence of noise.
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title_short	DCEMRI.jl: a fast, validated, open source toolkit for dynamic contrast enhanced MRI analysis
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