Geometric modeling of hepatic arteries in 3D ultrasound with unsupervised MRA fusion during liver interventions

  • Maxime Gérard
  • François Michaud
  • Alexandre Bigot
  • An Tang
  • Gilles Soulez
  • Samuel Kadoury
Original Article



Modulating the chemotherapy injection rate with regard to blood flow velocities in the tumor-feeding arteries during intra-arterial therapies may help improve liver tumor targeting while decreasing systemic exposure. These velocities can be obtained noninvasively using Doppler ultrasound (US). However, small vessels situated in the liver are difficult to identify and follow in US. We propose a multimodal fusion approach that non-rigidly registers a 3D geometric mesh model of the hepatic arteries obtained from preoperative MR angiography (MRA) acquisitions with intra-operative 3D US imaging.


The proposed fusion tool integrates 3 imaging modalities: an arterial MRA, a portal phase MRA and an intra-operative 3D US. Preoperatively, the arterial phase MRA is used to generate a 3D model of the hepatic arteries, which is then non-rigidly co-registered with the portal phase MRA. Once the intra-operative 3D US is acquired, we register it with the portal MRA using a vessel-based rigid initialization followed by a non-rigid registration using an image-based metric based on linear correlation of linear combination. Using the combined non-rigid transformation matrices, the 3D mesh model is fused with the 3D US.


3D US and multi-phase MRA images acquired from 10 porcine models were used to test the performance of the proposed fusion tool. Unimodal registration of the MRA phases yielded a target registration error (TRE) of \(1.2\pm 0.6\) mm. Initial rigid alignment of the portal MRA and 3D US yielded a mean TRE of \(8.6\pm 3.0\) mm, which was significantly reduced to \(4.0\pm 1.0\) mm (\(p = 0.002\)) after affine image-based registration. The following deformable registration step allowed for further decrease of the mean TRE to \(3.9\pm 1.1\) mm.


The proposed tool could facilitate visualization and localization of these vessels when using 3D US intra-operatively for either intravascular or percutaneous interventions to avoid vessel perforation.


Image registration Fusion Magnetic resonance angiography Ultrasound Vessel segmentation 



The authors would like to thank Hélène Héon and her team for their help during the animal experimentations and in collecting the animal information.

Funding This study was funded by the Canada Research Chairs (950-228359), the Canadian Institutes of Health Research (CIHR) (MOP340909), the Fonds de Recherche du Quebec en Science et Technologies (FRQNT) and the MEDITIS training program at Ecole Polytechnique de Montréal.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interests.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. All applicable international, national, and/or institutional guidelines for the care and use of animals were followed. All procedures performed in studies involving animals were in accordance with the ethical standards of the institution or practice at which the studies were conducted.


  1. 1.
    Chen HS, Gross JF (1980) Intra-arterial infusion of anticancer drugs: theoretic aspects of drug delivery and review of responses. Cancer Treat Rep 64(1):31–40PubMedGoogle Scholar
  2. 2.
    Varela M, Real MI, Burrel M, Forner A, Sala M, Brunet M, Ayuso C, Castells L, Montañá X, Llovet JM, Bruix J (2007) Chemoembolization of hepatocellular carcinoma with drug eluting beads: efficacy and doxorubicin pharmacokinetics. J Hepatol 46(3):474–481CrossRefPubMedGoogle Scholar
  3. 3.
    Draghi F, Rapaccini GL, Fachinetti C, de Matthaeis N, Battaglia S, Abbattista T, Busilacchi (2007) Ultrasound examination of the liver. J Ultrasound 10:5–11CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Chen SJ, Reinertsen I, Coupé P, Yan CX, Mercier L, Del Maestro DR, Collins DL (2012) Validation of hybrid Doppler ultrasound vessel-based registration algorithm for neurosurgery. Int J Comput Assist Radiol Surg 7(5):667–685CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Penney GP, Blackall JM, Hamady MS, Sabharwal T, Adam A, Hawkes DJ (2004) Registration of freehand 3D ultrasound and magnetic resonance liver images. Med Image Anal 8(1):81–91CrossRefPubMedGoogle Scholar
  6. 6.
    Milko S, Melvaer EL, Samset E, Kadir T (2009) A novel method for registration of US/MR of the liver based on the analysis of US dynamics. Med Image Comput Assist Interv 12:771–778Google Scholar
  7. 7.
    Wein W, Ladikos A, Fuerst B, Shah A, Sharma K, Navab N (2013) Global registration of ultrasound to MRI using the \(\text{LC}^2\) metric for enabling neurosurgical guidance. In: Medical image computing and computer-assisted intervention—MICCAI 2013, part I, volume 8149 of lecture notes in computer science. Springer, pp 33–41Google Scholar
  8. 8.
    Fuerst B, Wein W, Müller M, Navab N (2014) Automatic ultrasound-MRI registration for neurosurgery using the 2D and 3D \(\text{ LC }^2\) metric. Med Image Anal 18:1312–1319CrossRefPubMedGoogle Scholar
  9. 9.
    Rivaz H, Chen SJ, Collins DL (2015) Automatic deformable MR-ultrasound registration for image-guided neurosurgery. IEEE Trans Med Imaging 34(2):366–380CrossRefPubMedGoogle Scholar
  10. 10.
    Badoual A, Gerard M, De Leener B, Abi-Jaoudeh N, Kadoury S (2016) 3D vascular path planning of chemo-embolizations using segmented hepatic arteries from MR angiography. In: IEEE 13th international symposium on biomedical imaging (ISBI 2016), pp 225–228Google Scholar
  11. 11.
    Sato Y, Nakajima S, Atsumi H, Koller T, Gerig G, Yoshida S, Kikinis R (1997) 3D multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. In: CVRMed-MRCAS’97, volume 1205 of lecture notes in computer science. Springer, pp 213–222Google Scholar
  12. 12.
    De Leener B, Cohen-Adad J, Kadoury S (2015) Automatic segmentation of the spinal cord and spinal canal coupled with vertebral labeling. IEEE Trans Med Imaging 34:1705–1718CrossRefPubMedGoogle Scholar
  13. 13.
    Friman O, Hidennach M, Kühnel C, Peitgen HO (2010) Multiple hypothesis template tracking of small 3D vessel structures. Med Image Anal 14:160–171CrossRefPubMedGoogle Scholar
  14. 14.
    Klein S, Staring M, Murphy K, Viergever MA, Pluim JPW (2010) Elastix: a toolbox for intensity based medical image registration. IEEE Trans Med Imaging 29(1):196–205CrossRefPubMedGoogle Scholar
  15. 15.
    Shamonin DP, Bron EE, Lelieveldt BPF, Smits M, Klein S, Staring M (2014) Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer’s disease. Front Neuroinform 7(50):1–15Google Scholar
  16. 16.
    Pluim JPW, Maintz JBA, Viergever MA (2003) Mutual information based registration of medical images: a survey. IEEE Trans Med Imaging 22(8):986–1004CrossRefPubMedGoogle Scholar
  17. 17.
    Frangi AF, Niessen WJ, Vincken KL, Viergever MA (1998) Multiscale vessel enhancement filtering. In: Wells WM, Colchester A, Delp SL (eds) Medical image computing and computer-assisted intervention—MICCAI’98, volume 1496 of lecture notes in computer science. Springer, New York, pp 130–137Google Scholar
  18. 18.
    Powell MJ (2009) The BOBYQA algorithm for bound constrained optimization without derivatives. In: Cambridge report NA2009/06. University of CambridgeGoogle Scholar
  19. 19.
    Kadoury S, Zargochev L, Wood BJ, Venkatesan A, Weese J, Jago J, Kruecker J (2012) A model-based registration approach of pre-operative MRI with 3D ultrasound of the liver for Interventional guidance procedures. In: Proceedings of international symposium on biomedical imaging, pp 952–955Google Scholar
  20. 20.
    Wein W, Brunke S, Khamene A, Callstrom MR, Navab N (2008) Automatic CT-ultrasound registration for diagnostic imaging and image-guided intervention. Med Image Anal 12:577–585CrossRefPubMedGoogle Scholar
  21. 21.
    Abi-Jaoudeh N, Kobeiter H, Xu S, Wood BJ (2013) Image fusion during vascular and nonvascular image-guided procedures. Tech Vasc Interv Radiol 16(3):168–176CrossRefPubMedGoogle Scholar
  22. 22.
    Yzet T, Bouzerar R, Allart J-D, Demuynck F, Legallais C, Robert B, Deramond H, Meyer M-E, Balédent O (2010) Hepatic vascular flow measurements by phase contrast mri and doppler echography: a comparative and reproducibility study. J Magn Reson Imaging 31(3):579–588CrossRefPubMedGoogle Scholar
  23. 23.
    Yzet T, Bouzerar R, Baledent O, Renard C, Lumbala DM, Nguyen-Khac E, Regimbeau JM, Deramond H, Meyer M-E (2010) Dynamic measurements of total hepatic blood flow with phase contrast mri. Eur J Radiol 73(1):119–124CrossRefPubMedGoogle Scholar
  24. 24.
    Roldán-Alzate A, Frydrychowicz A, Niespodzany E, Landgraf BR, Johnson KM, Wieben O, Reeder SB (2013) In vivo validation of 4D flow MRI for assessing the hemodynamics of portal hypertension. J Magn Reson Imaging 37(5):1100–1108CrossRefPubMedGoogle Scholar
  25. 25.
    Berzigotti A, Reverter E, Garcia-Criado A, Abraldes JG, Cerini F, Garcia-Pagan JC, Bosch J (2013) Reliability of the estimation of total hepatic blood flow by doppler ultrasound in patients with cirrhotic portal hypertension. J Hepatol 59(4):717–722CrossRefPubMedGoogle Scholar
  26. 26.
    Bowdle A (2014) Vascular complications of central venous catheter placement: evidence-based methods for prevention and treatment. J Cardiothorac Vasc Anesth 28:358–368CrossRefPubMedGoogle Scholar
  27. 27.
    Pratschke S, Meimarakis G, Mayr S, Graeb C, Rentsch M, Zachoval R, Bruns CJ, Kleespies A, Jauch K-W, Loehe F, Angele MK (2011) Arterial bloof flow predicts graft survival in liver transplant patients. Liver Transpl 17:436–445CrossRefPubMedGoogle Scholar

Copyright information

© CARS 2017

Authors and Affiliations

  1. 1.Institute of Biomedical EngineeringPolytechnique MontréalMontrealCanada
  2. 2.Department of PhysicsUniversité de MontréalMontrealCanada
  3. 3.Department of Radiology, Radio-Oncology and Nuclear MedicineUniversité de MontréalMontrealCanada
  4. 4.Centre de recherche du Centre hospitalier de l’Université de Montréal (CRCHUM)MontrealCanada
  5. 5.Departement of RadiologyCentre Hospitalier de l’Université de MontréalMontrealCanada
  6. 6.Department of Computer EngineeringPolytechnique MontréalMontrealCanada

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