MITK-ModelFit: A generic open-source framework for model fits and their exploration in medical imaging - design, implementation and application on the example of DCE-MRI.
Dynamic PET
Model fitting
Multi-purpose
Pharmacokinetic modeling
Software development
Tracer-kinetics
Journal
BMC bioinformatics
ISSN: 1471-2105
Titre abrégé: BMC Bioinformatics
Pays: England
ID NLM: 100965194
Informations de publication
Date de publication:
16 Jan 2019
16 Jan 2019
Historique:
received:
01
08
2018
accepted:
19
12
2018
entrez:
18
1
2019
pubmed:
18
1
2019
medline:
13
2
2019
Statut:
epublish
Résumé
Many medical imaging techniques utilize fitting approaches for quantitative parameter estimation and analysis. Common examples are pharmacokinetic modeling in dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI)/computed tomography (CT), apparent diffusion coefficient calculations and intravoxel incoherent motion modeling in diffusion-weighted MRI and Z-spectra analysis in chemical exchange saturation transfer MRI. Most available software tools are limited to a special purpose and do not allow for own developments and extensions. Furthermore, they are mostly designed as stand-alone solutions using external frameworks and thus cannot be easily incorporated natively in the analysis workflow. We present a framework for medical image fitting tasks that is included in the Medical Imaging Interaction Toolkit MITK, following a rigorous open-source, well-integrated and operating system independent policy. Software engineering-wise, the local models, the fitting infrastructure and the results representation are abstracted and thus can be easily adapted to any model fitting task on image data, independent of image modality or model. Several ready-to-use libraries for model fitting and use-cases, including fit evaluation and visualization, were implemented. Their embedding into MITK allows for easy data loading, pre- and post-processing and thus a natural inclusion of model fitting into an overarching workflow. As an example, we present a comprehensive set of plug-ins for the analysis of DCE MRI data, which we validated on existing and novel digital phantoms, yielding competitive deviations between fit and ground truth. Providing a very flexible environment, our software mainly addresses developers of medical imaging software that includes model fitting algorithms and tools. Additionally, the framework is of high interest to users in the domain of perfusion MRI, as it offers feature-rich, freely available, validated tools to perform pharmacokinetic analysis on DCE MRI data, with both interactive and automatized batch processing workflows.
Sections du résumé
BACKGROUND
BACKGROUND
Many medical imaging techniques utilize fitting approaches for quantitative parameter estimation and analysis. Common examples are pharmacokinetic modeling in dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI)/computed tomography (CT), apparent diffusion coefficient calculations and intravoxel incoherent motion modeling in diffusion-weighted MRI and Z-spectra analysis in chemical exchange saturation transfer MRI. Most available software tools are limited to a special purpose and do not allow for own developments and extensions. Furthermore, they are mostly designed as stand-alone solutions using external frameworks and thus cannot be easily incorporated natively in the analysis workflow.
RESULTS
RESULTS
We present a framework for medical image fitting tasks that is included in the Medical Imaging Interaction Toolkit MITK, following a rigorous open-source, well-integrated and operating system independent policy. Software engineering-wise, the local models, the fitting infrastructure and the results representation are abstracted and thus can be easily adapted to any model fitting task on image data, independent of image modality or model. Several ready-to-use libraries for model fitting and use-cases, including fit evaluation and visualization, were implemented. Their embedding into MITK allows for easy data loading, pre- and post-processing and thus a natural inclusion of model fitting into an overarching workflow. As an example, we present a comprehensive set of plug-ins for the analysis of DCE MRI data, which we validated on existing and novel digital phantoms, yielding competitive deviations between fit and ground truth.
CONCLUSIONS
CONCLUSIONS
Providing a very flexible environment, our software mainly addresses developers of medical imaging software that includes model fitting algorithms and tools. Additionally, the framework is of high interest to users in the domain of perfusion MRI, as it offers feature-rich, freely available, validated tools to perform pharmacokinetic analysis on DCE MRI data, with both interactive and automatized batch processing workflows.
Identifiants
pubmed: 30651067
doi: 10.1186/s12859-018-2588-1
pii: 10.1186/s12859-018-2588-1
pmc: PMC6335810
doi:
Substances chimiques
Contrast Media
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
31Subventions
Organisme : National Center for Tumor diseases
ID : NCT 3.0-2015.22
Organisme : Deutsche Forschungsgemeinschaft
ID : KFO-214
Organisme : Bundesministerium für Bildung und Forschung
ID : 01IB13001B
Organisme : Deutsche Krebshilfe
ID : Max-Eder 108876
Organisme : Deutsche Forschungsgemeinschaft
ID : SFB/TRR 125
Références
Ann Neurol. 1979 Nov;6(5):371-88
pubmed: 117743
Nat Rev Neurosci. 2003 Jun;4(6):469-80
pubmed: 12778119
J Digit Imaging. 2004 Sep;17(3):205-16
pubmed: 15534753
Phys Med Biol. 2005 May 7;50(9):N85-92
pubmed: 15843726
AJNR Am J Neuroradiol. 2005 Oct;26(9):2213-7
pubmed: 16219824
Nucl Med Biol. 2006 Apr;33(3):287-94
pubmed: 16631076
Magn Reson Med. 2006 Nov;56(5):993-1000
pubmed: 17036301
Br J Cancer. 2007 Jan 29;96(2):189-95
pubmed: 17211479
Eur J Radiol. 2010 Dec;76(3):304-13
pubmed: 20363574
J Comput Assist Tomogr. 1991 Jul-Aug;15(4):621-8
pubmed: 2061479
Phys Med Biol. 2012 Jan 21;57(2):R1-33
pubmed: 22173205
Invest Radiol. 2012 Apr;47(4):252-8
pubmed: 22373532
Br J Radiol. 2011 Dec;84 Spec No 2:S112-20
pubmed: 22433822
J Digit Imaging. 2013 Apr;26(2):344-52
pubmed: 22832894
Radiology. 2013 Mar;266(3):698-700
pubmed: 23431225
J Pharmacokinet Pharmacodyn. 2013 Jun;40(3):281-300
pubmed: 23563847
Int J Comput Assist Radiol Surg. 2013 Jul;8(4):607-20
pubmed: 23588509
J Magn Reson Imaging. 2013 Dec;38(6):1554-63
pubmed: 23857776
BMC Bioinformatics. 2013 Nov 04;14:316
pubmed: 24180558
F1000Res. 2013 Dec 30;2:288
pubmed: 24555116
Transl Oncol. 2014 Feb 01;7(1):153-66
pubmed: 24772219
Med Phys. 2014 Dec;41(12):124301
pubmed: 25471985
PeerJ. 2015 Apr 23;3:e909
pubmed: 25922795
BMC Med Imaging. 2015 Jun 16;15:19
pubmed: 26076957
J Magn Reson Imaging. 2016 Jun;43(6):1288-300
pubmed: 26687041
JACC Cardiovasc Imaging. 2016 Jan;9(1):67-81
pubmed: 26762877
BMC Med Imaging. 2016 Jan 14;16:7
pubmed: 26767969
J Magn Reson Imaging. 2018 Jan;47(1):11-27
pubmed: 28792646
Phys Med Biol. 2017 Nov 21;62(24):9322-9340
pubmed: 28858856
Am J Physiol. 1982 Jul;243(1):R1-6
pubmed: 7091383
J Neurochem. 1977 May;28(5):897-916
pubmed: 864466
J Nucl Med. 1999 Jan;40(1):205-12
pubmed: 9935078