Skip to main content
SpringerLink
Log in

Image Processing in Contrast-Enhanced MR Angiography

  • Chapter
Magnetic Resonance Angiography

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Lee VS, Doug JM, Krinsky GA, Rofsky NM (2000) Gadolinium-enhanced MR angiography: artifacts and pitfalls. AJR Am J Roentgenol 175:197–205

    Article  CAS  Google Scholar 

  2. Barbier C, Lefevre F, Bui P et al (2001) Contrast-enhanced MRA of the carotid arteries using 0.5 Tesla: comparison with selective digital angiography. J Radiol 82(3 Pt 1):245–249

    CAS  PubMed  Google Scholar 

  3. Vanninen RL, Manninen HI, Partanen PK, et al (1996) How should we estimate carotid stenosis using magnetic resonance angiography? Neuroradiology 38(4):299–305

    Article  CAS  Google Scholar 

  4. Young GR, Humphrey PR, Nixon TE, Smith ET (1996) Variability in measurement of extracranial internal carotid artery stenosis as displayed by both digital subtraction and magnetic resonance angiography: an assessment of three caliper techniques and visual impression of stenosis. Stroke 27(3):467–473

    Article  CAS  Google Scholar 

  5. Ruehm SG, Goyen M, Barkhausen et al (2001) Rapid magnetic resonance angiography for detection of atherosclerosis. Lancet 357(9262):1086–1091

    Article  CAS  Google Scholar 

  6. Wong KS, Lam WW, Liang E et al (1996) Variability of magnetic resonance angiography and computed tomography angiography in grading middle cerebral artery stenosis. Stroke 27(6):1084–1087

    Article  CAS  Google Scholar 

  7. Elgersma OE, Wust AF, Buijs PC et al (2000) Multidirectional depiction of internal carotid arterial stenosis: three-dimensional time-of-flight MR angiography versus rotational and conventional digital subtraction angiography. Radiology 216(2):511–516

    Article  CAS  Google Scholar 

  8. Clunie DA (2000) DICOM Structured Reporting. Bangor: PixelMed Publishing

    Google Scholar 

  9. Philips Medical Systems. DICOM Cook Book. Available at: ftp://ftp-wjq.philips.com/medical/interoperability/out/DICOM_Information/CookBook.pdf

    Google Scholar 

  10. Horii SC (1997) Primer on computers and information technology. Part four: A nontechnical introduction to DICOM. Radiographics 17:1297–1309

    Article  CAS  Google Scholar 

  11. ACR and NEMA Digital Imaging and Communications Standards Committee. The DICOM V3.0 standard. Available at http://medical.nema.org/Dicom/Geninfo/brochure/BROCH95.DOC

    Google Scholar 

  12. NEMA’s official DICOM web page. http://medical.nema.org/dicom.html

    Google Scholar 

  13. eFilm Workstation by eFilm Medical Inc. http://www.efilm.ca

    Google Scholar 

  14. Zhao M, Charbel FT, Alperin N et al (2000) Improved phase-contrast flow quantification by three-dimensional vessel localization. Magn Reson Imaging 18(6):697–706

    Article  CAS  Google Scholar 

  15. Gutberlet M, Hosten N, Beier J et al (1999) Quantification of the degree of a stenosis using multiplanar reformation (MPR) of a magnetic resonance angiography (MRA) data set and 2-dimensional MR images compared with MR-flow measurement in patients with coarcation. In CARS’99 Computer Assisted Radiology and Surgery. H.U. Lemke, M.W. Vannier, K. Inamura and A.G. Farman (Eds) Amsterdam, The Netherlands. Elsevier 124–128

    Google Scholar 

  16. Calhoun PS, Kuszyk BS, Heath DG et al (1999) Three-dimensional volume rendering of spiral CT data: theory and method. Radiographics; 19:745–764

    Article  CAS  Google Scholar 

  17. Hany TF, Schmidt M, Davis CP, et al (1998) Diagnostic impact of four postprocessing techniques in evaluating contrast-enhanced three-dimensional MR angiography. AJR Am J Roentgenol 170:907–912

    Article  CAS  Google Scholar 

  18. Anderson CM, Saloner D, Tsuruda JS et al (1990) Artifacts in maximum intensity projection display of MR angiograms. AJR Am J Roentgenol 154(3):623–629

    Article  CAS  Google Scholar 

  19. Orkisz M, Bresson C, Magnin IE et al (1997) Improved vessel visualization in MR angiography by nonlinear anisotropic filtering. Magn Reson Med 37:914–919

    Article  CAS  Google Scholar 

  20. Anderson CM (1993) Postprocessing and display. In: CM Anderson, RR. Edelman and P. Turski, (Eds) Clinical magnetic resonance angiography. New York: Raven Press 83–98

    Google Scholar 

  21. Vitrea by VitalImages. http://www.vitalimages.com

    Google Scholar 

  22. Wise SW, Hopper KD, Ten Have T, Schwartz T (1998) Measuring carotid artery stenosis using CT angiography: the dilemme of artifactual lumen eccentricity. AJR Am J Roentgenol 170(4):919–923

    Article  CAS  Google Scholar 

  23. Hu X, Alperin N, Levin DN et al (1991) Visualization of MR angiographic data with segmentation and volume-rendering techniques. J Magn Reson Imaging 1(5):539–546

    Article  CAS  Google Scholar 

  24. Wilson DL, Noble JA (1997) Segmentation of cerebral vessels and aneurysms from MR angiography data. In Information Processing in Medical Imaging. (Lecture Notes in Computer Science). G. Goos, J. Hartmanis and J. van Leeuwen, Eds. Springer-Verlag, Heidelberg, Germany 1230:423–428

    Chapter  Google Scholar 

  25. Masutani Y, Schiemann T, Höne KH (1998) Vascular shape segmentation and structure extraction using a shape-based region-growing model. In MICCAI’ 98 Medical Image Computing & Computer-Assisted Intervention (Lecture Notes in Computer Science). A. Colchester, W.M. Wells and S. Delp, Eds. New York: Springer-Verlag 1496:1242–1249

    Google Scholar 

  26. Chung ACS, Noble JA (1999) Statistical 3D vessel segmentation using a Rician distribution. In: Taylor C, Colchester A. (Eds) MICCAI’99 Medical Image Computing & Computer-Assisted Intervention (Lecture Notes in Computer Science). Springer Berlin Heidelberg, New York, pp 1679:82–89

    Chapter  Google Scholar 

  27. Verdonck B, Bloch I, Maître H et al (1996) Accurate segmentation of blood vessels from 3D medical images. In ICIP’96 IEEE Int. Conf. On Image Processing. Lausanne vol. III:311–314

    Article  Google Scholar 

  28. Nazarian B, Chédot C, Sequeira J et al (1996) Automatic reconstruction of irregular tubular structures using generalized cylinders. Revue de CFAO et d’informatique graphique 11:11–20

    Google Scholar 

  29. Lorenz C, Carlsen IC, Buzug TM et al (1997) Multiscale line segmentation with automatic estimation of width, contrast and tangential direction in 2D and 3D medical images. In: Trocazz J, Grimson E, Mösges R (Eds) CVRMed/MRCAS’97, (Lecture Notes in Computer Science). Springer Berlin Heidelberg, New York, pp 233–242

    Google Scholar 

  30. Wink O, Niessen WJ, Viergever MA (1998) Fast quantification of abdominal aorta aneurysms from CTA volumes. In: Colchester A, Wells WM, Delp S (Eds) MICCAI’98 Medical Image Computing & Computer-Assisted Intervention (Lecture Notes in Computer Science). Springer Berlin Heidelberg, New York, pp 1496:138–145

    Chapter  Google Scholar 

  31. Wang KC, Dutton RW, Taylor CA (1999) Improving geometric model construction for blood flow modeling. IEEE Eng Med Biol Mag 18(6):33–39

    Article  CAS  Google Scholar 

  32. Swift RD, Ramaswamy K, Higgins WE (1997) Adaptive axes generation algorithm for 3D tubular structures. In ICIP’97 IEEE Int. Conf. On Image Processing. Sta Barbara vol. II:136–139

    Article  Google Scholar 

  33. Orkisz M, Hernández-Hoyos M, Douek P, Magnin I (2000) Advances of blood vessel morphology analysis in 3D magnetic resonance images. Machine Graphics & Vision 9:463–471

    Google Scholar 

  34. Frangi AF, Niessen WJ, Hoogeveen RM et al (1999) Model-Based quantitation of 3-D magnetic resonance angiographic images. IEEE Trans Med Imaging 18(10):946–956

    Article  CAS  Google Scholar 

  35. Bulpitt AJ, Berry E (1998) An automatic 3D deformable model for segmentation of branching structures compared with interactive region growing. In: Berry E (Ed) Proceedings of Medical Image Understanding and Analysis’98. pp 189–192

    Google Scholar 

  36. DicomEye by ETIAM. http://www.etiam.com

    Google Scholar 

  37. Osiris by the University Hospital of Geneva. http://www.expasy.ch/www/UIN/html1/projects/osiris/osiris.html

    Google Scholar 

  38. MediMatic Dicom Viewer by MediMatic. Http://www.medimatic.com

    Google Scholar 

  39. MRI Flex Trial by General Electric. http://apps.gemedicalsystems.com/geCommunity/aw/FlexTrial/awmr_flextrial_home.jsp

    Google Scholar 

  40. Leonardo Workstation by Siemens. http://siemensmedical.com/

    Google Scholar 

  41. EasyVision by Philips. http://www.philips.com/ms

    Google Scholar 

  42. Marchand B, Hernández-Hoyos M, Orkisz M, Douek P (2001) Diagnostic des Sténoses de l’Artère Rénale en Angiographie par Résonance Magnétique et Appréciation du Degré de Sténose. J Mal Vasc 25:(5) 312–320 (in French)

    Google Scholar 

  43. Hernández-Hoyos M, Anwander A, Orkisz M et al (2000) A deformable vessel model with single point initialization for segmentation, quantification and visualization of blood vessels in 3D MRA. In MICCAI’00 Medical Image Computing & Computer-Assisted Intervention (Lecture Notes in Computer Science). S.L. Delp, A.M. Digioia and B. Jaramaz Eds. Berlin, Germany: Springer-Verlag 1935:735–745

    Google Scholar 

  44. Hernández-Hoyos M, Orkisz M, Roux JP, Douek P (1999) Inertia-based vessel axis extraction and stenosis quantification in 3D MRA images. In CARS’99 Computer Assisted Radiology and Surgery. H.U. Lemke, M.W. Vannier, K. Inamura and A.G. Farman (Eds) Amsterdam, The Netherlands. Elsevier.; 189–193

    Google Scholar 

  45. McInerney T, Terzopoulos D (1996) Deformable models in medical image analysis: a survey. Med Image Anal 1(2):91–108

    Article  CAS  Google Scholar 

  46. Kass M, Witkin A, Terzopoulos D (1988) Snakes: active contour models. IJCV 1:321–331

    Article  Google Scholar 

  47. Cohen LD (1991) On Active Contour Models and Balloons. CVGIP 53(2):211–218

    Article  Google Scholar 

  48. Renaudin CP, Barbier B, Roriz R et al (1994) Coronary Arteries: New Design for Three-dimensional Arterial Phantoms. Radiology 190:579–582

    Article  CAS  Google Scholar 

  49. Sato Y, Nakajima S, Atsumi H et al (1997) 3D Multiscale line filter for segmentation and visualization of curvilinear structures in medical images. In CVRMed/MRCAS’97, (Lecture Notes in Computer Science) J. Trocazz, E. Grimson and R. Mösges (Eds) Berlin. Germany: Springer-Verlag 213–222

    Google Scholar 

  50. Prinet V, Monga O (1997) Vessel representation in 2D and 3D angiograms. In CARS’97 Computer Assisted Radiology and Surgery. HU Lemke, MW Vannier and K. Inamura Eds. Amsterdam, The Netherlands. Elsevier 240–255

    Google Scholar 

Download references

Authors
  1. Philippe C. Douek
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marcela Hernández-Hoyos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maciej Orkisz
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Diagnostic and Interventional Radiology, Saarland University Hospital, Homburg/Saar, Germany

    Günther Schneider

  2. Department of Radiology, Weill Medical College of Cornell University, New York, USA

    Martin R. Prince

  3. Department of Radiology, Columbia College of Physicians and Surgeons, New York, USA

    Martin R. Prince

  4. MRI Department, St. James’s Hospital, Dublin, Ireland

    James F. M. Meaney

  5. Department of Radiology and Radiological Sciences, Uniformed Services University of the Health Sciences, Bethesda, USA

    Vincent B. Ho

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Italia

About this chapter

Cite this chapter

Douek, P.C., Hernández-Hoyos, M., Orkisz, M. (2005). Image Processing in Contrast-Enhanced MR Angiography. In: Schneider, G., Prince, M.R., Meaney, J.F.M., Ho, V.B. (eds) Magnetic Resonance Angiography. Springer, Milano. https://doi.org/10.1007/88-470-0352-0_4

Download citation

  • DOI: https://doi.org/10.1007/88-470-0352-0_4

  • Publisher Name: Springer, Milano

  • Print ISBN: 978-88-470-0266-1

  • Online ISBN: 978-88-470-0352-1

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation