Skip to main content
SpringerLink
Log in

DiffeoRaptor: diffeomorphic inter-modal image registration using RaPTOR

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Diffeomorphic image registration is essential in many medical imaging applications. Several registration algorithms of such type have been proposed, but primarily for intra-contrast alignment. Currently, efficient inter-modal/contrast diffeomorphic registration, which is vital in numerous applications, remains a challenging task.

Methods

We proposed a novel inter-modal/contrast registration algorithm that leverages Robust PaTch-based cOrrelation Ratio metric to allow inter-modal/contrast image alignment and bandlimited geodesic shooting demonstrated in Fourier-Approximated Lie Algebras (FLASH) algorithm for fast diffeomorphic registration.

Results

The proposed algorithm, named DiffeoRaptor, was validated with three public databases for the tasks of brain and abdominal image registration while comparing the results against three state-of-the-art techniques, including FLASH, NiftyReg, and Symmetric image Normalization (SyN).

Conclusions

Our results demonstrated that DiffeoRaptor offered comparable or better registration performance in terms of registration accuracy. Moreover, DiffeoRaptor produces smoother deformations than SyN in inter-modal and contrast registration. The code for DiffeoRaptor is publicly available at https://github.com/nimamasoumi/DiffeoRaptor.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Hernandez M, Bossa MN, Olmos S (2009) Registration of anatomical images using paths of diffeomorphisms parameterized with stationary vector field flows. Int J Comput Vis 85:291–306

    Article  Google Scholar 

  2. Tward D, Brown T, Kageyama Y, Patel J, Hou Z, Mori S, Albert M, Troncoso J, Miller M (2020) Diffeomorphic registration with intensity transformation and missing data: application to 3D digital pathology of Alzheimer’s disease. Front Neurosci 14:52

    Article  Google Scholar 

  3. Zhang M, Golland P (2016) Statistical shape analysis: from landmarks to diffeomorphisms. In: Medical image analysis, ISSN: 1361-8415, vol 33, pp 155–158

  4. Blaiotta C, Freund P, Cardoso MJ, Ashburner J (2018) Generative diffeomorphic modelling of large MRI data sets for probabilistic template construction. NeuroImage 166:117–134

    Article  Google Scholar 

  5. Xu Z, Lee CP, Heinrich MP, Modat M, Rueckert D, Ourselin S, Abramson RG, Landman BA (2016) Evaluation of six registration methods for the human abdomen on clinically acquired CT. IEEE Trans Biomed Eng 63(8):1563–1572

    Article  Google Scholar 

  6. Beg M, Miller M, Trouvé A, Younes L (2005) Computing large deformation metric mappings via geodesic flows of diffeomorphisms. Int J Comput Vis 61(2):139–157

    Article  Google Scholar 

  7. Vialard FX, Risser L, Rueckert D, Cotter CJ (2012) Diffeomorphic 3D image registration via geodesic shooting using an efficient adjoint calculation. Int J Comput Vis 97(2):229–241

    Article  Google Scholar 

  8. Zhang M, Fletcher PT (2019) Fast diffeomorphic image registration via Fourier-approximated lie algebras. Int J Comput Vis 127:61–73

    Article  Google Scholar 

  9. Wu J, Tang X (2020) A large deformation diffeomorphic framework for fast brain image registration via parallel computing and optimization. Neuroinformatics 18:251–266

    Article  Google Scholar 

  10. Chen X, Diaz-Pinto A, Ravikumar N, Frangi AF (2021) Deep learning in medical image registration. Prog Biomed Eng 3:012003

    Google Scholar 

  11. Dalca AV, Balakrishnan G, Guttag J, Sabuncu MR (2019) Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces. Med Image Anal 57:226–236

    Article  Google Scholar 

  12. Wang J, Zhang M (2020) Deepflash: an efficient network for learning-based medical image registration. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 4444–4452

  13. Hering A, Hansen L, Mok TC, Chung A, Siebert H, Her S, Lange A, Kuckertz S, Heldmann S, Shao W, Vesal S (2021) Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning. arXiv preprint arXiv:2112.04489

  14. Mitra J, Kato Z, Martí R, Oliver A, Lladó X, Sidibé D, Ghose S, Vilanova JC, Comet J, Meriaudeau F (2012) A spline-based non-linear diffeomorphism for multimodal prostate registration. Med Image Anal 16(6):1259–1279

    Article  Google Scholar 

  15. Kutten KS, Charon N, Miller MI, Ratnanather JT, Mateisky J, Baden AD (2017) A large deformation diffeomorphic approach to registration of CLARITY images via mutual information. In: Medical image computing and computer assisted intervention—MICCAI, 2017, vol 10433. Lecture Notes in Computer Science. Springer, Cham

  16. Reaungamornrat S, Silva TD, Uneri A, Vogt S, Kleinszig G, Khanna AJ, Wolinsky JP, Prince JL, Siewerdsen JH (2016) MIND demons: symmetric diffeomorphic deformable registration of MR and CT for image-guided spine surgery. IEEE Trans Med Imaging 35(11):2413–2424

    Article  Google Scholar 

  17. Avants BB, Epstein CL, Grossman M, Gee JC (2008) Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal 12(1):26–41

    Article  CAS  Google Scholar 

  18. Vercauteren T, Pennec X, Perchant A, Ayache N (2009) Diffeomorphic demons: efficient non-parametric image registration. Neuroimage 45(1 Suppl):S61-72

    Article  Google Scholar 

  19. Heinrich MP, Jenkinson M, Bhushan M, Matin T, Gleeson FV, Brady M, Schnabel JA (2012) MIND: modality independent neighbourhood descriptor for multi-modal deformable registration. Med Image Anal 16(7):1423–1435

    Article  Google Scholar 

  20. Rivaz H, Chen SJ, Collins DL (2015) Automatic deformable MR-ultrasound registration for image-guided neurosurgery. IEEE Trans Med Imaging 34(2):366–80

    Article  Google Scholar 

  21. Mercier L, Maestro RFD, Petrecca K, Araujo D, Haegelen C, Collins DL (2012) Online database of clinical MR and ultrasound images of brain tumors. Med Phys 39:3253

    Article  Google Scholar 

  22. Masoumi N, Xiao Y, Rivaz H (2019) ARENA: inter-modality affine registration using evolutionary strategy. Int J Comput Assist Radiol Surg 14:441–450

    Article  Google Scholar 

  23. Xiao Y, Fortin M, Unsgård G, Rivaz H, Reinertsen I (2017) REtroSpective evaluation of cerebral tumors (RESECT): a clinical database of pre-operative MRI and intra-operative ultrasound in low-grade glioma surgeries. Med Phys 44:3875–3882

    Article  Google Scholar 

  24. Masoumi N, Belasso CJ, Ahmad MO, Benali H, Xiao Y, Rivaz H (2021) Multimodal 3D ultrasound and CT in image-guided spinal surgery: public database and new registration algorithms. Int J Comput Assist Radiol Surg 16:555–565

    Article  Google Scholar 

  25. Singh N, Hinkle J, Joshi S, Fletcher PT (2013) A vector momenta formulation of diffeomorphisms for improved geodesic regression and atlas construction. In: IEEE 10th international symposium on biomedical imaging, pp 1219–1222

  26. Oliveira FPM, Tavares JMRS (2014) Medical image registration: a review. Comput Methods Biomech Biomed Eng 17(2):73–93

    Article  Google Scholar 

  27. Modat M, Ridgway GR, Taylor ZA, Lehmann M, Barnes J, Hawkes DJ, Fox NC, Ourselin S (2010) Fast free-form deformation using graphics processing units. Comput Methods Program Biomed 98(3):278–284

    Article  Google Scholar 

  28. Miller MI, Trouvé A, Younes L (2006) Geodesic shooting for computational anatomy. J Math Imaging Vis 24:209–228

    Article  Google Scholar 

  29. Joshi SC, Miller MI (2000) Landmark matching via large deformation diffeomorphisms. IEEE Trans Image Process 9(8):1357–1370

    Article  CAS  Google Scholar 

  30. Roche A, Malandain G, Pennec X, Ayache N (1998) The correlation ratio as a new similarity measure for multimodal image registration. In: Medical image computing and computer-assisted intervention—MICCAI98, Lecture Notes in Computer Science, vol 1496. Springer, Berlin, Heidelberg

  31. LaMontagne PJ, Benzinger TL, Morris JC, Keefe S, Hornbeck R, Xiong C, Grant E, Hassenstab J, Moulder K, Vlassenko A Raichle ME (2019) OASIS-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and Alzheimer disease. MedRxiv

  32. Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F (2013) The cancer imaging archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 26(6):1045–1057

    Article  Google Scholar 

  33. Avants BB, Tustison N, Song G (2009) Advanced normalization tools (ANTS). Insight j 2(365):1–35

    Google Scholar 

  34. Manjón JV, Coupé P (2016) volBrain: an online MRI brain volumetry system. Front Neuroinform 10:30

    Article  Google Scholar 

  35. Manjón JV, Eskildsen S, Coupé P, Romero J, Collins L, Robles M (2014) NICE: nonlocal intracranial cavity extraction. Int J Biomed Imaging 4191:820205

  36. Grabner G, Janke AL, Budge MM, Smith D, Pruessner J, Collins DL (2006) Symmetric atlasing and model based segmentation: an application to the hippocampus in older adults. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv 9:58–66

    Google Scholar 

  37. Rohlfing T (2011) Image similarity and tissue overlaps as surrogates for image registration accuracy: widely used but unreliable. IEEE Trans Med Imaging 31(2):153–163

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) and in part by the Regroupment Strategique en Microelectronique du Quebec. Data were provided in part by OASIS-3: Principal Investigators: T. Benzinger, D. Marcus, J. Morris; NIH P50 AG00561, P30 NS09857781, P01 AG026276, P01 AG003991, R01 AG043434, UL1 TR000448, R01 EB009352. AV-45 doses were provided by Avid Radiopharmaceuticals, a wholly owned subsidiary of Eli Lilly.

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Concordia University, Montreal, Quebec, Canada

    Nima Masoumi, Hassan Rivaz & M. Omair Ahmad

  2. Department of Computer Science and Software Engineering, Concordia University, Montreal, Quebec, Canada

    Yiming Xiao

Authors
  1. Nima Masoumi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hassan Rivaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Omair Ahmad
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yiming Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nima Masoumi.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest.

Ethical standard

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 Declaration of Helsinki and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all participants included in the study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file 1 (pdf 377 KB)

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Masoumi, N., Rivaz, H., Ahmad, M.O. et al. DiffeoRaptor: diffeomorphic inter-modal image registration using RaPTOR. Int J CARS 18, 367–377 (2023). https://doi.org/10.1007/s11548-022-02749-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-022-02749-2

Keywords

Search

Navigation