Ummperfusion: An open source software tool towards quantitative mri perfusion analysis in clinical routine

Journal of Digital Imaging

Volumn 26, Issue 2, 2013, Pages 344-352

  Zöllner, Frank G ^a   Weisser, Gerald ^b   Reich, Marcel ^a,b   Kaiser, Sven ^a,b   Schoenberg, Stefan O ^b   Sourbron, Steven P ^c   Schad, Lothar R ^a  

^a UNIVERSITY OF HEIDELBERG   (Germany)

^b UNIVERSITY OF HEIDELBERG   (Germany)

^c UNIVERSITY OF LEEDS   (United Kingdom)

Author keywords

Algorithms; Computer assisted; Image processing; Magnetic resonance imaging; Perfusion

Indexed keywords

ALGORITHMS; IMAGE PROCESSING; MAGNETIC RESONANCE IMAGING; OPEN SOURCE SOFTWARE; PARALLEL ARCHITECTURES; PIXELS; PLASMA DEVICES; PLASMA FLOW; PLASMA JETS; QUALITY MANAGEMENT; SOFTWARE ENGINEERING;

CLINICAL ENVIRONMENTS; COMPUTER ASSISTED; DECONVOLUTION APPROACH; IMPLEMENTATION DESIGN; PERFUSION; PHYSIOLOGICAL PROCESS; QUANTITATIVE PARAMETERS; REFERENCE IMPLEMENTATION;

QUALITY CONTROL;

ARTICLE; COMPUTER ASSISTED DIAGNOSIS; COMPUTER PROGRAM; DIFFUSION WEIGHTED IMAGING; HUMAN; MALE; METHODOLOGY; SCINTIGRAPHY;

DIFFUSION MAGNETIC RESONANCE IMAGING; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MALE; PERFUSION IMAGING; SOFTWARE;

EID: 84878767635     PISSN: 08971889     EISSN: 1618727X     Source Type: Journal    
DOI: 10.1007/s10278-012-9510-6     Document Type: Article

References (54)

1. Thng CH, Koh TS, Collins DJ, Koh DM: Perfusion magnetic resonance imaging of the liver. World J Gastroenterol 16:1598-1609, 2010
2. Scherr MK, Seitz M, Muller-Lisse UG, Ingrisch M, Reiser MF, Muller-Lisse UL: MR-perfusion (MRP) and diffusion-weighted imaging (DWI) in prostate cancer: quantitative and model-based gadobenate dimeglumine MRP parameters in detection of prostate cancer. Eur J Radiol 76:359-366, 2010
3. Attenberger UI, et al: Comprehensive MR evaluation of renal disease: added clinical value of quantified renal perfusion values over single MR angiography. J Magn Reson Imaging 31:125-133, 2010
4. Bokacheva L, Rusinek H, Zhang JL, Lee VS: Assessment of renal function with dynamic contrast-enhanced MR imaging. Magn Reson Imaging Clin N Am 16:597-611, 2008. viii
5. Sourbron S: Compartmental modelling for magnetic resonance renography. Z Med Phys 20:101-114, 2010
6. Sourbron SP, Buckley DL: Tracer kinetic modelling in MRI: estimating perfusion and capillary permeability. Phys Med Biol 57:R1-R33, 2012
7. Thompson G, Mills SJ, Stivaros SM, Jackson A: Imaging of brain tumors: perfusion/permeability. Neuroimaging Clin N Am 20:337-353, 2010
8. Tofts PS, et al: Estimating kinetic parameters from dynamic contrastenhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging 10:223-232, 1999
9. Ostergaard L: Principles of cerebral perfusion imaging by bolus tracking. J Magn Reson Imaging 22:710-717, 2005
10. 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 24:1117-1123, 2006
11. Attenberger U, et al.: Clinical Value of MR-Based Quantification of Renal Perfusion Parameters with a Separable Two-Compartment Model. Proceedings 16th Scientific Meeting, International Society for Magnetic Resonance in Medicine, Toronto:3680, 2008
12. Miller WL, Hodge DO, Tointon SK, Rodeheffer RJ, Nelson SM, Gibbons RJ: Relationship of myocardial perfusion imaging findings to outcome of patients with heart failure and suspected ischemic heart disease. Am Heart J 147:714-720, 2004
13. Futterer JJ, et al: Prostate cancer: local staging at 3-T endorectal MR imaging-early experience. Radiology 238:184-191, 2006
14. Futterer JJ, et al: Prostate cancer localization with dynamic contrast-enhanced MR imaging and proton MR spectroscopic imaging. Radiology 241:449-458, 2006
15. Litjens GJS, Vos PC, Barentsz JO, Karssemeijer N, Huisman HJ: Automatic computer aided detection of abnormalities in multiparametric prostate MRI. SPIE, Orlando, 2011
16. Weidner AM, et al: Value of multiparametric prostate MRI of the peripheral zone. Z Med Phys 21:198-205, 2011
17. Sorensen AG: Perfusion MR imaging: moving forward. Radiology 249:416-417, 2008
18. Grenier N, et al: Measurement of glomerular filtration rate with magnetic resonance imaging: principles, limitations, and expectations. Semin Nucl Med 38:47-55, 2008
19. Sourbron S: Technical aspects of MR perfusion. Eur J Radiol 76:304-313, 2010
20. 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 74:e154-e160, 2010
21. Zöllner FG, 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 33:171-181, 2009
22. Lietzmann F, Zöllner FG, Michaely HJ, Schad LR: Untersuchung von selbstnavigierenden MR-Sequenzen für die Perfusionsbildgebung der Nieren. Z Med Phys 20:124-133, 2010
23. Kosior JC, Frayne R: PerfTool: a software platform for investigating bolus-tracking perfusion imaging quantification strategies. J Magn Reson Imaging 25:653-659, 2007
24. Sourbron S, Biffar A, Ingrisch M, Fierens Y, Luypaert R: PMI: platform for research in medical imaging. Magn Reson Mater Phy 22:539, 2009
25. Wittsack HJ, Ritzl A, Modder U: User friendly analysis of MR investigations of the cerebral perfusion: Windows(R)-based image processing. Rofo 174:742-746, 2002
26. Ferl GZ: DATforDCEMRI: an R package for deconvolution analysis and visualization of DCE-MRI data. J Stat Softw 44:1-18, 2011
27. Whitcher B, Schmid VJ: Quantitative analysis of dynamic contrast-enhanced and diffusion-weighted magnetic resonance imaging for oncology in R. J Stat Softw 44:1-29, 2011
28. 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 4:1-10, 2009
29. Caban JJ, Joshi A, Nagy P: Rapid development of medical imaging tools with open-source libraries. J Digit Imaging 20 (Suppl 1):83-93, 2007
30. Sourbron S, Dujardin M, Makkat S, Luypaert R: Pixel-by-pixel deconvolution of bolus-tracking data: optimization and implementation. Phys Med Biol 52:429-447, 2007
31. Østergaard L, Weisskoff RM, Chesler DA, Gyldensted C, Rosen BR: High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part I: mathematical approach and statistical analysis. Magn Reson Med 36:715-725, 1996
32. Sourbron S, Luypaert R, Morhard D, Seelos K, Reiser M, Peller M: Deconvolution of bolus-tracking data: a comparison of discretization methods. Phys Med Biol 52:6761-6778, 2007
33. Rosset A, Spadola L, Ratib O: OsiriX: an open-source software for navigating in multidimensional DICOM images. J Digit Imaging 17:205-216, 2004
34. Jalbert F, Paoli JR: Osirix: free and open-source software for medical imagery. Rev Stomatol Chir 109:53-55, 2008
35. Rosset A, Spadola L, Pysher L, Ratib O: Informatics in radiology (infoRAD)navigating the fifth dimension: innovative interface for multidimensional multimodality image navigation. Radiographics 26:299-308, 2006
36. OsiriX Plugins. Available at http://www. osirix-viewer. com/ Plugins. html. Accessed 24 Jan 2011
37. Ratib O, Rosset A, Heuberger J: Open Source software and social networks: disruptive alternatives for medical imaging. Eur J Radiol 78:259-265, 2011
38. Vetter S: Optimising an OsiriX Plug-in for Perfusion Analysis of MR Images Applied University Mannheim, Faculty of Computer Science, Bachelor of Science, Bachelor's Thesis (24. 9. 2010 2010)
39. Apple Inc.: Introducing Blocks and Grand Central Dispatch, http:// developer. apple. com/library/mac/#featuredarticles/BlocksGCD/ index. html: Apple Inc., 2010
40. Weidenhagen R, Meirnarakis G, Jauch KW, Becker CR, Kopp R: OsiriX. An open-source solution for planning aorta stent grafts. Gefässchirurgie 13:278-290, 2008
41. Yamauchi T, et al: Efficacy and reliability of highly functional open source DICOM software (OsiriX) in spine surgery. J Clin Neurosci 17:756-759, 2010
42. Calamante F, Gadian DG, Connelly A: Quantification of perfusion using bolus tracking magnetic resonance imaging in stroke: assumptions, limitations, and potential implications for clinical use. Stroke 33:1146-1151, 2002
43. Jerosch-Herold M, Seethamraju RT, Swingen CM, Wilke NM, Stillman AE: Analysis of myocardial perfusion MRI. J Magn Reson Imaging 19:758-770, 2004
44. Ali B, Hsiao E, Di Carli MF: Combined anatomic and perfusion imaging of the heart. Curr Cardiol Rep 12:90-97, 2010
45. Ohno Y, et al: Primary pulmonary hypertension: 3D dynamic perfusion MRI for quantitative analysis of regional pulmonary perfusion. AJR Am J Roentgenol 188:48-56, 2007
46. Zöllner FG, Zahn K, Schaible T, Schoenberg SO, Schad LR, Neff KW: Quantitative pulmonary perfusion imaging at 3. 0 T of 2-yearold children after congenital diaphragmatic hernia repair: initial results. Eur Radiol, 2012. doi:10. 1007/s00330-012-2528-9
47. Makkat S, Luypaert R, Sourbron S, Stadnik T, De Mey J: Quantification of perfusion and permeability in breast tumors with a deconvolution-based analysis of second-bolus T1-DCE data. J Magn Reson Imaging 25:1159-1167, 2007
48. Aumann S, et al: Quantification of renal perfusion using an intravascular contrast agent (part 1): results in a canine model. Magn Reson Med 49:276-287, 2003
49. Eastwood JD, et al: CT perfusion scanning with deconvolution analysis: pilot study in patients with acute middle cerebral artery stroke. Radiology 222:227-236, 2002
50. Bisdas S, et al: A comparison of tumour perfusion assessed by deconvolution-based analysis of dynamic contrast-enhanced CT and MR imaging in patients with squamous cell carcinoma of the upper aerodigestive tract. Eur Radiol 18:843-850, 2008
51. Sourbron S, Ingrisch M, Siefert A, Reiser M, Herrmann K: Quantification of cerebral blood flow, cerebral blood volume, and blood-brain-barrier leakage with DCE-MRI. Magn Reson Med 62:205-217, 2009
52. Padhani AR: Dynamic contrast-enhanced MRI in clinical oncology: current status and future directions. J Magn Reson Imaging 16:407-422, 2002
53. Lietzmann F, Zöllner FG, Attenberger UI, Haneder S, Michaely HJ, Schad LR: DCE-MRI of the human kidney using BLADE: a feasibility study in healthy volunteers. J Magn Reson Imaging 35:868-874, 2011
54. Hansen PC: Rank-Deficient and Discrete Ill-Posed Problems: Numerical Aspects of Linear Inversion. SIAM, Philadelphia, 1998