Skip to main content
Top
Published in: BMC Medical Imaging 1/2016

Open Access 01-12-2016 | Software

An open source software for analysis of dynamic contrast enhanced magnetic resonance images: UMMPerfusion revisited

Authors: Frank G. Zöllner, Markus Daab, Steven P. Sourbron, Lothar R. Schad, Stefan O. Schoenberg, Gerald Weisser

Published in: BMC Medical Imaging | Issue 1/2016

Login to get access

Abstract

Background

Perfusion imaging has become an important image based tool to derive the physiological information in various applications, like tumor diagnostics and therapy, stroke, (cardio-) vascular diseases, or functional assessment of organs. However, even after 20 years of intense research in this field, perfusion imaging still remains a research tool without a broad clinical usage. One problem is the lack of standardization in technical aspects which have to be considered for successful quantitative evaluation; the second problem is a lack of tools that allow a direct integration into the diagnostic workflow in radiology.

Results

Five compartment models, namely, a one compartment model (1CP), a two compartment exchange (2CXM), a two compartment uptake model (2CUM), a two compartment filtration model (2FM) and eventually the extended Toft’s model (ETM) were implemented as plugin for the DICOM workstation OsiriX. Moreover, the plugin has a clean graphical user interface and provides means for quality management during the perfusion data analysis. Based on reference test data, the implementation was validated against a reference implementation. No differences were found in the calculated parameters.

Conclusion

We developed open source software to analyse DCE-MRI perfusion data. The software is designed as plugin for the DICOM Workstation OsiriX. It features a clean GUI and provides a simple workflow for data analysis while it could also be seen as a toolbox providing an implementation of several recent compartment models to be applied in research tasks. Integration into the infrastructure of a radiology department is given via OsiriX. Results can be saved automatically and reports generated automatically during data analysis ensure certain quality control.
Literature
1.
go back to reference Michaely H, Sourbron S, Dietrich O, Attenberger U, Reiser M, Schoenberg S. Functional renal MR imaging: an overview. Abdom Imaging. 2007;32(6):758–71.PubMedCrossRef Michaely H, Sourbron S, Dietrich O, Attenberger U, Reiser M, Schoenberg S. Functional renal MR imaging: an overview. Abdom Imaging. 2007;32(6):758–71.PubMedCrossRef
2.
go back to reference Koh TS, Bisdas S, Koh DM, Thng CH. Fundamentals of tracer kinetics for dynamic contrast-enhanced MRI. J Magn Reson Imaging. 2011;34(6):1262–76.PubMedCrossRef Koh TS, Bisdas S, Koh DM, Thng CH. Fundamentals of tracer kinetics for dynamic contrast-enhanced MRI. J Magn Reson Imaging. 2011;34(6):1262–76.PubMedCrossRef
3.
go back to reference Sourbron S. Technical aspects of MR perfusion. Eur J Radiol. 2010.76(3):304-13. Sourbron S. Technical aspects of MR perfusion. Eur J Radiol. 2010.76(3):304-13.
4.
go back to reference Ingrisch M, Sourbron S. Tracer-kinetic modeling of dynamic contrast-enhanced MRI and CT: a primer. J Pharmacokinet Phar. 2013;40(3):281–300.CrossRef Ingrisch M, Sourbron S. Tracer-kinetic modeling of dynamic contrast-enhanced MRI and CT: a primer. J Pharmacokinet Phar. 2013;40(3):281–300.CrossRef
5.
go back to reference Cuenod CA, Balvay D. Perfusion and vascular permeability: basic concepts and measurement in DCE-CT and DCE-MRI. Diagnostic Int imaging. 2013;94(12):1187–204.CrossRef Cuenod CA, Balvay D. Perfusion and vascular permeability: basic concepts and measurement in DCE-CT and DCE-MRI. Diagnostic Int imaging. 2013;94(12):1187–204.CrossRef
6.
go back to reference Wildner D, Pfeifer L, Goertz RS, Bernatik T, Sturm J, Neurath MF, et al. Dynamic contrast-enhanced ultrasound (DCE-US) for the characterization of hepatocellular carcinoma and cholangiocellular carcinoma. Ultraschall Der Medizin. 2014;35(6):522–7.CrossRef Wildner D, Pfeifer L, Goertz RS, Bernatik T, Sturm J, Neurath MF, et al. Dynamic contrast-enhanced ultrasound (DCE-US) for the characterization of hepatocellular carcinoma and cholangiocellular carcinoma. Ultraschall Der Medizin. 2014;35(6):522–7.CrossRef
7.
go back to reference Attenberger U, Michaely H, Sourbron S, Notohamiprodjio M, Glaser C, Reiser M, et al. Clinical value of MR-based quantification of renal perfusion parameters with a separable two-compartment model. Toronto: Proceedings 16th Scientific Meeting, International Society for Magnetic Resonance in Medicine; 2008. p. 3680. Attenberger U, Michaely H, Sourbron S, Notohamiprodjio M, Glaser C, Reiser M, et al. Clinical value of MR-based quantification of renal perfusion parameters with a separable two-compartment model. Toronto: Proceedings 16th Scientific Meeting, International Society for Magnetic Resonance in Medicine; 2008. p. 3680.
8.
go back to reference Zöllner FG, Zimmer F, Klotz S, Hoeger S, Schad LR. Functional imaging of acute kidney injury at 3 Tesla: investigating multiple parameters using DCE-MRI and a two-compartment filtration model. Z Med Phys. 2015;25(1):58–65.PubMedCrossRef Zöllner FG, Zimmer F, Klotz S, Hoeger S, Schad LR. Functional imaging of acute kidney injury at 3 Tesla: investigating multiple parameters using DCE-MRI and a two-compartment filtration model. Z Med Phys. 2015;25(1):58–65.PubMedCrossRef
9.
go back to reference Buckley DL, Shurrab AAE, Cheung CM, Jones AP, Mamtora H, Kalra PA. Measurement of single kidney function using dynamic contrast-enhanced MRI: comparison of two models in human subjects. J Magn Reson Imaging. 2006;24(5):1117–23.PubMedCrossRef Buckley DL, Shurrab AAE, Cheung CM, Jones AP, Mamtora H, Kalra PA. Measurement of single kidney function using dynamic contrast-enhanced MRI: comparison of two models in human subjects. J Magn Reson Imaging. 2006;24(5):1117–23.PubMedCrossRef
11.
go back to reference Biglands JD, Magee DR, Sourbron SP, Plein S, Greenwood JP, Radjenovic A. Comparison of the diagnostic performance of four quantitative myocardial perfusion estimation methods used in cardiac mr imaging: ce-marc substudy. Radiology. 2015;275(2):393–402.PubMedPubMedCentralCrossRef Biglands JD, Magee DR, Sourbron SP, Plein S, Greenwood JP, Radjenovic A. Comparison of the diagnostic performance of four quantitative myocardial perfusion estimation methods used in cardiac mr imaging: ce-marc substudy. Radiology. 2015;275(2):393–402.PubMedPubMedCentralCrossRef
12.
13.
go back to reference Franiel T, Hamm B, Hricak H. Dynamic contrast-enhanced magnetic resonance imaging and pharmacokinetic models in prostate cancer. Eur Radiol. 2011;21(3):616–26.PubMedCrossRef Franiel T, Hamm B, Hricak H. Dynamic contrast-enhanced magnetic resonance imaging and pharmacokinetic models in prostate cancer. Eur Radiol. 2011;21(3):616–26.PubMedCrossRef
14.
go back to reference Messner NM, Zollner FG, Kalayciyan R, Schad LR. Pre-clinical functional magnetic resonance imaging part II: The heart. Z Med Phys. 2014;24(4):307–22.PubMedCrossRef Messner NM, Zollner FG, Kalayciyan R, Schad LR. Pre-clinical functional magnetic resonance imaging part II: The heart. Z Med Phys. 2014;24(4):307–22.PubMedCrossRef
15.
go back to reference Zollner FG, Kalayciyan R, Chacon-Caldera J, Zimmer F, Schad LR. Pre-clinical functional magnetic resonance imaging part I: The kidney. Z Med Phys. 2014;24(4):286–306.PubMedCrossRef Zollner FG, Kalayciyan R, Chacon-Caldera J, Zimmer F, Schad LR. Pre-clinical functional magnetic resonance imaging part I: The kidney. Z Med Phys. 2014;24(4):286–306.PubMedCrossRef
16.
go back to reference Jaspers K, Leiner T, Dijkstra P, Oostendorp M, van Golde JM, Post MJ, et al. Optimized pharmacokinetic modeling for the detection of perfusion differences in skeletal muscle with DCE-MRI: effect of contrast agent size. Med Phys. 2010;37(11):5746–55. Jaspers K, Leiner T, Dijkstra P, Oostendorp M, van Golde JM, Post MJ, et al. Optimized pharmacokinetic modeling for the detection of perfusion differences in skeletal muscle with DCE-MRI: effect of contrast agent size. Med Phys. 2010;37(11):5746–55.
17.
go back to reference Ng CS, Wei W, Bankson JA, Ravoori MK, Han L, Brammer DW, et al. Dependence of DCE-MRI biomarker values on analysis algorithm. PLoS One. 2015;10(7), e0130168.PubMedPubMedCentralCrossRef Ng CS, Wei W, Bankson JA, Ravoori MK, Han L, Brammer DW, et al. Dependence of DCE-MRI biomarker values on analysis algorithm. PLoS One. 2015;10(7), e0130168.PubMedPubMedCentralCrossRef
18.
go back to reference Cutajar M, Mendichovszky IA, Tofts PS, Gordon I. The importance of AIF ROI selection in DCE-MRI renography: reproducibility and variability of renal perfusion and filtration. Eur J Radiol. 2010;74(3):e154–60.PubMedCrossRef Cutajar M, Mendichovszky IA, Tofts PS, Gordon I. The importance of AIF ROI selection in DCE-MRI renography: reproducibility and variability of renal perfusion and filtration. Eur J Radiol. 2010;74(3):e154–60.PubMedCrossRef
19.
go back to reference Mendichovszky IA, Cutajar M, Gordon I. Reproducibility of the aortic input function (AIF) derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the kidneys in a volunteer study. Eur J Radiol. 2009;71(3):576–81.PubMedCrossRef Mendichovszky IA, Cutajar M, Gordon I. Reproducibility of the aortic input function (AIF) derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the kidneys in a volunteer study. Eur J Radiol. 2009;71(3):576–81.PubMedCrossRef
20.
go back to reference Zöllner FG, Sance R, Rogelj P, Ledesma-Carbayo MJ, Rørvik J, Santos A, et al. Assessment of 3D DCE-MRI of the kidneys using non-rigid image registration and segmentation of voxel time courses. Comput Med Imaging Graph. 2009;33(3):171–81.PubMedCrossRef Zöllner FG, Sance R, Rogelj P, Ledesma-Carbayo MJ, Rørvik J, Santos A, et al. Assessment of 3D DCE-MRI of the kidneys using non-rigid image registration and segmentation of voxel time courses. Comput Med Imaging Graph. 2009;33(3):171–81.PubMedCrossRef
21.
go back to reference Hodneland E, Hanson EA, Lundervold A, Modersitzki J, Eikefjord E, Munthe-Kaas AZ. Segmentation-driven image registration- application to 4D DCE-MRI recordings of the moving kidneys. IEEE Trans Image Process. 2014;23(5):2392–404.PubMedCrossRef Hodneland E, Hanson EA, Lundervold A, Modersitzki J, Eikefjord E, Munthe-Kaas AZ. Segmentation-driven image registration- application to 4D DCE-MRI recordings of the moving kidneys. IEEE Trans Image Process. 2014;23(5):2392–404.PubMedCrossRef
23.
go back to reference European Society of R. ESR position paper on imaging biobanks. Insights Imaging. 2015;6(4):403–10.CrossRef European Society of R. ESR position paper on imaging biobanks. Insights Imaging. 2015;6(4):403–10.CrossRef
26.
go back to reference Heye T, Davenport MS, Horvath JJ, Feuerlein S, Breault SR, Bashir MR, et al. Reproducibility of dynamic contrast-enhanced MR imaging. Part I. Perfusion characteristics in the female pelvis by using multiple computer-aided diagnosis perfusion analysis solutions. Radiology. 2013;266(3):801–11.PubMedCrossRef Heye T, Davenport MS, Horvath JJ, Feuerlein S, Breault SR, Bashir MR, et al. Reproducibility of dynamic contrast-enhanced MR imaging. Part I. Perfusion characteristics in the female pelvis by using multiple computer-aided diagnosis perfusion analysis solutions. Radiology. 2013;266(3):801–11.PubMedCrossRef
27.
go back to reference Wittsack HJ, Ritzl A, Modder U. User friendly analysis of MR investigations of the cerebral perfusion: Windows(R)-based image processing. Röfo. 2002;174(6):742–6.PubMed Wittsack HJ, Ritzl A, Modder U. User friendly analysis of MR investigations of the cerebral perfusion: Windows(R)-based image processing. Röfo. 2002;174(6):742–6.PubMed
28.
go back to reference Goh V, Schaeffter T, Leach M. Reproducibility of dynamic contrast-enhanced MR imaging: why we should care. Radiology. 2013;266(3):698–700.PubMedCrossRef Goh V, Schaeffter T, Leach M. Reproducibility of dynamic contrast-enhanced MR imaging: why we should care. Radiology. 2013;266(3):698–700.PubMedCrossRef
29.
go back to reference Jalbert F, Paoli JR. Osirix: Free and open-source software for medical imagery. Rev Stomatol Chir. 2008;109(1):53–5.CrossRef Jalbert F, Paoli JR. Osirix: Free and open-source software for medical imagery. Rev Stomatol Chir. 2008;109(1):53–5.CrossRef
30.
go back to reference Ruggiero S, Weisser G. Integrating Mac systems into a medical IT infrastructure: creating an affordable radiology workstation with OsiriX. Mannheim: Department of Clinical Radiology, University Hospital of Mannheim; 2007. p. 19. Ruggiero S, Weisser G. Integrating Mac systems into a medical IT infrastructure: creating an affordable radiology workstation with OsiriX. Mannheim: Department of Clinical Radiology, University Hospital of Mannheim; 2007. p. 19.
31.
go back to reference Zöllner FG, Weisser G, Reich M, Kaiser S, Schoenberg SO, Sourbron SP, et al. UMMPerfusion: an open source software tool towards quantitative MRI perfusion analysis in clinical routine. J Digit Imaging. 2013;26(2):344–52.PubMedPubMedCentralCrossRef Zöllner FG, Weisser G, Reich M, Kaiser S, Schoenberg SO, Sourbron SP, et al. UMMPerfusion: an open source software tool towards quantitative MRI perfusion analysis in clinical routine. J Digit Imaging. 2013;26(2):344–52.PubMedPubMedCentralCrossRef
33.
34.
go back to reference Sourbron SP, Buckley DL. Classic models for dynamic contrast-enhanced MRI. NMR Biomed. 2013;26(8):1004–27.PubMedCrossRef Sourbron SP, Buckley DL. Classic models for dynamic contrast-enhanced MRI. NMR Biomed. 2013;26(8):1004–27.PubMedCrossRef
35.
go back to reference Sourbron S. Compartmental modelling for magnetic resonance renography. Z Med Phys. 2010;20(2):101–14.PubMedCrossRef Sourbron S. Compartmental modelling for magnetic resonance renography. Z Med Phys. 2010;20(2):101–14.PubMedCrossRef
36.
go back to reference Flouri D, Lesnic D, Sourbron S. Fitting the two-compartment model in DCE-MRI by linear inversion. 2015. Flouri D, Lesnic D, Sourbron S. Fitting the two-compartment model in DCE-MRI by linear inversion. 2015.
37.
go back to reference Sourbron SP, Buckley DL. Tracer kinetic modelling in MRI: estimating perfusion and capillary permeability. Phys Med Biol. 2012;57(2):R1–33.PubMedCrossRef Sourbron SP, Buckley DL. Tracer kinetic modelling in MRI: estimating perfusion and capillary permeability. Phys Med Biol. 2012;57(2):R1–33.PubMedCrossRef
38.
go back to reference Brix G, Kiessling F, Lucht R, Darai S, Wasser K, Delorme S, et al. Microcirculation and microvasculature in breast tumors: Pharmacokinetic analysis of dynamic MR image series. Magn Reson Med. 2004;52(2):420–9.PubMedCrossRef Brix G, Kiessling F, Lucht R, Darai S, Wasser K, Delorme S, et al. Microcirculation and microvasculature in breast tumors: Pharmacokinetic analysis of dynamic MR image series. Magn Reson Med. 2004;52(2):420–9.PubMedCrossRef
39.
go back to reference Markwardt CB. Non-linear least squares fitting in IDL with MPFIT, Astronomical data analysis software and systems XVIII: 2009; Quebec, Canada. San Francisco: Astronomical Society of the Pacific; 2009. p. 251–4. Markwardt CB. Non-linear least squares fitting in IDL with MPFIT, Astronomical data analysis software and systems XVIII: 2009; Quebec, Canada. San Francisco: Astronomical Society of the Pacific; 2009. p. 251–4.
40.
go back to reference Sourbron S, Biffar A, Ingrisch M, Fierens Y, Luypaert R. PMI: platform for research in medical imaging. Magn Reson Mater Phy. 2009;22(1):539. Sourbron S, Biffar A, Ingrisch M, Fierens Y, Luypaert R. PMI: platform for research in medical imaging. Magn Reson Mater Phy. 2009;22(1):539.
43.
go back to reference Luypaert R, Sourbron S, de Mey J. Validity of perfusion parameters obtained using the modified Tofts model: a simulation study. Magn Reson Med. 2011;65(5):1491–7.PubMedCrossRef Luypaert R, Sourbron S, de Mey J. Validity of perfusion parameters obtained using the modified Tofts model: a simulation study. Magn Reson Med. 2011;65(5):1491–7.PubMedCrossRef
44.
go back to reference Luypaert R, Ingrisch M, Sourbron S, de Mey J. The Akaike information criterion in DCE-MRI: does it improve the haemodynamic parameter estimates? Phys Med Biol. 2012;57(11):3609–28.PubMedCrossRef Luypaert R, Ingrisch M, Sourbron S, de Mey J. The Akaike information criterion in DCE-MRI: does it improve the haemodynamic parameter estimates? Phys Med Biol. 2012;57(11):3609–28.PubMedCrossRef
46.
go back to reference Krasner GE, Pope ST. A cookbook for using the model-view controller user interface paradigm in Smalltalk-80. J Object Oriented Program. 1988;1(3):26–49. Krasner GE, Pope ST. A cookbook for using the model-view controller user interface paradigm in Smalltalk-80. J Object Oriented Program. 1988;1(3):26–49.
47.
go back to reference Kosior JC, Frayne R. PerfTool: a software platform for investigating bolus-tracking perfusion imaging quantification strategies. J Magn Reson Imaging. 2007;25(3):653–9.PubMedCrossRef Kosior JC, Frayne R. PerfTool: a software platform for investigating bolus-tracking perfusion imaging quantification strategies. J Magn Reson Imaging. 2007;25(3):653–9.PubMedCrossRef
48.
go back to reference Puech P, Betrouni N, Makni N, Dewalle AS, Villers A, Lemaitre L. Computer-assisted diagnosis of prostate cancer using DCE-MRI data: design, implementation and preliminary results. Int J Comput Assist Radiol Surg. 2009;4(1):1–10.PubMedCrossRef Puech P, Betrouni N, Makni N, Dewalle AS, Villers A, Lemaitre L. Computer-assisted diagnosis of prostate cancer using DCE-MRI data: design, implementation and preliminary results. Int J Comput Assist Radiol Surg. 2009;4(1):1–10.PubMedCrossRef
49.
go back to reference Whitcher B, Schmid VJ. Quantitative analysis of dynamic contrast-enhanced and diffusion-weighted magnetic resonance imaging for oncology in R. J Stat Softw. 2011;44:1–29. Whitcher B, Schmid VJ. Quantitative analysis of dynamic contrast-enhanced and diffusion-weighted magnetic resonance imaging for oncology in R. J Stat Softw. 2011;44:1–29.
50.
go back to reference Ferl G. DATforDCEMRI: an R package for deconvolution analysis and visualization of DCE-MRI data. J Stat Softw. 2011;44:1–18.CrossRef Ferl G. DATforDCEMRI: an R package for deconvolution analysis and visualization of DCE-MRI data. J Stat Softw. 2011;44:1–18.CrossRef
51.
go back to reference Barnes SR, Ng TS, Santa-Maria N, Montagne A, Zlokovic BV, Jacobs RE. ROCKETSHIP: a flexible and modular software tool for the planning, processing and analysis of dynamic MRI studies. BMC Med Imaging. 2015;15:19.PubMedPubMedCentralCrossRef Barnes SR, Ng TS, Santa-Maria N, Montagne A, Zlokovic BV, Jacobs RE. ROCKETSHIP: a flexible and modular software tool for the planning, processing and analysis of dynamic MRI studies. BMC Med Imaging. 2015;15:19.PubMedPubMedCentralCrossRef
52.
go back to reference Ortuno JE, Ledesma-Carbayo MJ, Simoes RV, Candiota AP, Arus C, Santos A. DCE@urLAB: a dynamic contrast-enhanced MRI pharmacokinetic analysis tool for preclinical data. BMC Bioinf. 2013;14:316.CrossRef Ortuno JE, Ledesma-Carbayo MJ, Simoes RV, Candiota AP, Arus C, Santos A. DCE@urLAB: a dynamic contrast-enhanced MRI pharmacokinetic analysis tool for preclinical data. BMC Bioinf. 2013;14:316.CrossRef
53.
go back to reference Cron GO, Sourbron S, Barnoriak DP, Abdeen R, Hogan M, Nguyen TB. Bias and precision of three different DCE-MRI analysis software packages: a comparison using simulated data. Milan: Proceedings in Internaltional Conference for Magnetic Resonance in Medicine; 2014. p. 4592. Cron GO, Sourbron S, Barnoriak DP, Abdeen R, Hogan M, Nguyen TB. Bias and precision of three different DCE-MRI analysis software packages: a comparison using simulated data. Milan: Proceedings in Internaltional Conference for Magnetic Resonance in Medicine; 2014. p. 4592.
54.
go back to reference Beuzit L, Eliat P-A, Bannier E, Ferre J-C, Gandon Y, Brun V, et al. Dynamic contrast-enhanced MR imaging in rectal cancer: study of inter-software accuracy and reproducibility using simulated and clinical data. Toronto: Proceedings in International Conference for Magnetic Resonancen in Medicine; 2015. p. 789. Beuzit L, Eliat P-A, Bannier E, Ferre J-C, Gandon Y, Brun V, et al. Dynamic contrast-enhanced MR imaging in rectal cancer: study of inter-software accuracy and reproducibility using simulated and clinical data. Toronto: Proceedings in International Conference for Magnetic Resonancen in Medicine; 2015. p. 789.
55.
go back to reference Davenport MS, Heye T, Dale BM, Horvath JJ, Breault SR, Feuerlein S, et al. Inter- and intra-rater reproducibility of quantitative dynamic contrast enhanced MRI using TWIST perfusion data in a uterine fibroid model. J Magn Reson Imaging. 2013;38(2):329–35.PubMedCrossRef Davenport MS, Heye T, Dale BM, Horvath JJ, Breault SR, Feuerlein S, et al. Inter- and intra-rater reproducibility of quantitative dynamic contrast enhanced MRI using TWIST perfusion data in a uterine fibroid model. J Magn Reson Imaging. 2013;38(2):329–35.PubMedCrossRef
56.
go back to reference Lassel E, Daab M, Schülein P, Drechsler J, Schönberg S, Schad L, et al. In-Haus-MPG-Zertifizierung von Software in der Radiologie am Beispiel von UMMPerfusion. Fortschr Röntgenstr. 2013;185(S01):VO202_208. Lassel E, Daab M, Schülein P, Drechsler J, Schönberg S, Schad L, et al. In-Haus-MPG-Zertifizierung von Software in der Radiologie am Beispiel von UMMPerfusion. Fortschr Röntgenstr. 2013;185(S01):VO202_208.
57.
go back to reference Michaely HJ, Sourbron SP, Buettner C, Lodemann KP, Reiser MF, Schoenberg SO. Temporal constraints in renal perfusion imaging with a 2-compartment model. Invest Radiol. 2008;43(2):120–8.PubMedCrossRef Michaely HJ, Sourbron SP, Buettner C, Lodemann KP, Reiser MF, Schoenberg SO. Temporal constraints in renal perfusion imaging with a 2-compartment model. Invest Radiol. 2008;43(2):120–8.PubMedCrossRef
Metadata
Title
An open source software for analysis of dynamic contrast enhanced magnetic resonance images: UMMPerfusion revisited
Authors
Frank G. Zöllner
Markus Daab
Steven P. Sourbron
Lothar R. Schad
Stefan O. Schoenberg
Gerald Weisser
Publication date
01-12-2016
Publisher
BioMed Central
Published in
BMC Medical Imaging / Issue 1/2016
Electronic ISSN: 1471-2342
DOI
https://doi.org/10.1186/s12880-016-0109-0

Other articles of this Issue 1/2016

BMC Medical Imaging 1/2016 Go to the issue