Skip to main content
SpringerLink
Log in

A multi-metric registration strategy for the alignment of longitudinal brain images in pediatric oncology

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

Survival of pediatric patients with brain tumor has increased over the past 20 years, and increasing evidence of iatrogenic toxicities has been reported. In follow-ups, images are acquired at different time points where substantial changes of brain morphology occur, due to childhood physiological development and treatment effects. To address the image registration complexity, we propose two multi-metric approaches (Mplus, Mdot), combining mutual information (MI) and normalized gradient field filter (NGF). The registration performance of the proposed metrics was assessed on a simulated dataset (Brainweb) and compared with those obtained by MI and NGF separately, using mean magnitude and mean angular errors. The most promising metric (Mplus) was then selected and tested on a retrospective dataset comprising 45 pediatric patients who underwent focal radiotherapy for brain cancer. The quality of the realignment was scored by a radiation oncologist using a perceived misalignment metric (PM). All patients but one were assessed as PM ≤ 2 (good alignment), but the remaining one, severely affected by hydrocephalus and pneumocephalus at the first MRI acquisition, scored PM = 5 (unacceptable). These preliminary findings suggest that Mplus might improve the registration accuracy in complex applications such as pediatric oncology, when data are acquired throughout the years of follow-up, and is worth investigating.

Graphical abstract showing the clinical workflow of the overall registration procedure including the three rigid steps, the fourth deformable step, the reference MRI and the registered MRI as well as the contoured ROIs. The registration performance is assessed by means of the Perceived Misalignment score (PM).

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
Fig. 7

Similar content being viewed by others

References

  1. Horska A, Nidecker A, Intrapiromkul J, Tannazi F, Ardekani S, Brant LJ et al (2014) Diffusion tensor imaging of deep grey matter in children treated for brain malignancies. Childs Nerv Syst 30:631–638

    Article  Google Scholar 

  2. Wang X, Li L, Hu C, Qiu J, Xu Z (2009) A comparative study of three CT and MRI registration algorithms in nasopharyngeal carcinoma. J Appl Med Phys 10:3–10

    CAS  Google Scholar 

  3. Qian Y, Li M, Wei Q, Ren X (2014) A registration method for multimodal medical images using contours mutual information. Int J Adv Res Artif Intell 3:3–7

    Google Scholar 

  4. Lundqvist R, Bengtsson E, Thurfjell L (2003) A combined intensity and gradient-based similarity criterion for interindividual SPECT brain scan. Eurasip J Adv Signal Process 5:967364

    Article  Google Scholar 

  5. Song G, Han J, Zhao Y, Wang Z, Du H (2017) A review on medical image registration as an optimization problem. Curr Med Imaging Rev 13:274–283. https://doi.org/10.2174/1573405612666160920123955

    Article  PubMed  PubMed Central  Google Scholar 

  6. Hill DLG, Batchelor PG, Holden M, Hawkes DJ (2001) Medical image registration. Phys Med Biol 46:R1–R45

    Article  CAS  Google Scholar 

  7. Pluim JPW, Maintz AJB, Viergever MA (2000) Image registration by maximization of combined mutual information and gradient information. International Conference on Medical Image Computing and Computer-Assisted Intervention. pp 452–461

  8. Studholme C, Hill DLG, Hawkes DJ (1996) Automated 3-D registration of MR and CT images of the head. Med Image Anal 1:163–175

    Article  CAS  Google Scholar 

  9. D’Agostino E, Modersitzki J, Maes F, Vandermeulen D (2003) Free-form registration using mutual information and curvature regularization. International Workshop on Biomedical Image Registration, pp. 11–20

  10. Haber E, Modersitzki J (2005) Beyond mutual information : a simple and robust alternative. Bildverarbeitung für die Medizin, pp 350–354

  11. Haber E, Modersitzki J (2006) Intensity gradient based registration and fusion of multi-modal images. International Conference on Medical Image Computing and Computer-Assisted Intervention, pp 726–733

  12. Studholme C, Hill DLG, Hawkes DJ (1999) An overlap invariant entropy measure of 3D medical image alignment. Pattern Recogn 32:71–86

    Article  Google Scholar 

  13. Tang L, Hero A, Harmarneh G (2012) Locally-adaptive similarity metric for deformable medical image registration. International symposium on biomedical imaging, pp 728–731

  14. Woo J, Stone M, Prince JL (2015) Multimodal registration via mutual information incorporating geometric and spatial context. IEEE Trans Image Process 24:757–769. https://doi.org/10.1109/TIP.2014.2387019.Multimodal

    Article  PubMed  PubMed Central  Google Scholar 

  15. Montin E, Meroni S, Arrigoni F, Potepan P, Anna A, Pecori E et al (2012) Validation of an image registration framework to investigate long term neurocognitive outcome correlated to radiation dose and diffusion-tensor mri information (DTI) in children focally irradiated for primitive malignant brain tumours. Pediatr Blood Cancer 59:965–1152

    Article  Google Scholar 

  16. Montin E, Potepan P, Mainardi L (2012) A registration framework for evaluation of T1, T2 and DWI signal intensities in multiple myeloma. International conference on bio-inspired system and signal processing, pp 563–566

  17. Montin E, Messina A, Mainardi L (2013) A full automatic method for the soft tissues sarcoma treatment response based on fuzzy logic. XIII Mediterranean Conference on Medical and Biological Engineering and Computing, pp 221–224

  18. Montin E, Storelli L, Meroni S, Arrigoni F, Adducci A, Pecori E, et al (2014) Tuning and validation of an image registration procedure for pediatric MR images. 4th conference of Gruppo Nazionale di Bioingegneria, pp 25–27

  19. Montin E, Cutrì E, Spadola G, Testori A, Pennati G, Mainardi L (2015) Tuning of a deformable image registration procedure for skin component mechanical properties assessment. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), pp 6305–6308

  20. Ruhaak J, Konig L, Hallmann M, Papenberg N, Heldmann S, Schumacher H, et al (2013) A fully parallel algorithm for multimodal image registration using normalized gradient fields. International Symposium on Biomedical Imaging (ISBI), pp 572–575

  21. Wollny G, Ledesma-carbayo MJ, Kellman P, Santos A, Member S (2010) Exploiting quasiperiodicity in motion correction of free-breathing myocardial perfusion MRI. IEEE Trans Med Imaging 29:1516–1527

    Article  Google Scholar 

  22. Wollny G, Ledesma-carbayo MJ, Santos A (2010) An ITK implementation of the normalized gradient field image to image metric. Insight J

  23. Mattes D, Haynor DR, Vesselle H, Lewellyn TK, Eubank W (2001) Nonrigid multimodality image registration. Med Imaging 2001 Image Process. 4322: 1609–1621

  24. Byrd RH, Nocedal J, Schnabel RB (1994) Representations of quasi-Newton matrices and their use in limited memory methods. Math Program 63:129–156

    Article  Google Scholar 

  25. Yoo TS, Ackerman MJ, Lorensen WE, Schroeder W, Chalana V, Aylward S, Metaxas D, Whitaker R (2002) Engineering and algorithm design for an image processing API : a technical report on ITK - the insight toolkit. Stud Health Technol Inform 85:586–592

    PubMed  Google Scholar 

  26. Cocosco CA, Kollokian V, Kwan RK-S, Pike GB, Evans AC (1997) Brainweb: online interface to a 3D MRI simulated brain database. NeuroImage 5:425

  27. Collins DL, Zijdenbos AP, Kollokian V, Sled JG, Kabani NJ, Holmes CJ et al (1998) Design and construction of a realistic digital brain phantom. IEEE Trans Med Imaging 17:463–468

    Article  CAS  Google Scholar 

  28. Ou Y, Akbari H, Bilello M, Da X, Davatzikos C (2014) Comparative evaluation of registration algorithms in different brain databases with varying difficulty: results and insights. IEEE Trans Med Imaging IEEE 33:2039–2065. https://doi.org/10.1109/TMI.2014.2330355

    Article  Google Scholar 

  29. Stanley N, Glide-Hurst C, Kim J, Adams J, Li S, Wen N et al (2013) Using patient-specific phantoms to evaluate deformable image registration algorithms for adaptive radiation therapy. J Appl Clin Med Phys 14:177–194. https://doi.org/10.1120/jacmp.v14i6.4363

    Article  PubMed Central  Google Scholar 

  30. Stancanello J, Cavedon C, Francescon P, Cerveri P, Giancarlo F, Causin F et al (2005) CT-3D rotational angiography automatic registration : a sensitivity analysis. Med Biol Eng Comput 43:667–671

    Article  CAS  Google Scholar 

  31. Stancanello J, Romanelli P, Sebastiano F, Modugno N, Muacevic A, Cerveri P et al (2007) Direct validation of atlas-based red nucleus identification for functional radiosurgery. Med Phys 34:3143–3148. https://doi.org/10.1118/1.2750970031

    Article  PubMed  Google Scholar 

Download references

Funding

This study was partially funded by Associazione Italiana per la Ricerca sul Cancro (Investigator Grant 2013 N. 14486).

Author information

Authors and Affiliations

  1. Department of Electronics Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy

    Eros Montin, Antonella Belfatto, Marco Bologna, Pietro Cerveri & Luca Mainardi

  2. Fondazione IRCCS Istituto Nazionale dei Tumori, Medical Physics Unit, Milan, Italy

    Silvia Meroni, Claudia Cavatorta & Emanuele Pignoli

  3. Pediatric Radiotherapy Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy

    Emilia Pecori, Barbara Diletto & Lorenza Gandola

  4. PediatricUnit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy

    Maura Massimino

  5. Neuro-oncological Unit and Neuropsychological Rehabilitation Unit Scientific Institute, IRCCS E. Medea, Bosisio Parini, Lecco, Italy

    Maria Chiara Oprandi & Geraldina Poggi

  6. Neuroimaging Lab, Scientific Institute, IRCCS E. Medea Bosisio Parini, Lecco, Italy

    Filippo Arrigoni & Denis Peruzzo

Authors
  1. Eros Montin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Antonella Belfatto
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marco Bologna
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Silvia Meroni
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Claudia Cavatorta
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Emilia Pecori
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Barbara Diletto
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Maura Massimino
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Maria Chiara Oprandi
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Geraldina Poggi
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Filippo Arrigoni
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Denis Peruzzo
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Emanuele Pignoli
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Lorenza Gandola
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Pietro Cerveri
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Luca Mainardi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Antonella Belfatto.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Montin, E., Belfatto, A., Bologna, M. et al. A multi-metric registration strategy for the alignment of longitudinal brain images in pediatric oncology. Med Biol Eng Comput 58, 843–855 (2020). https://doi.org/10.1007/s11517-019-02109-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-019-02109-4

Keywords

Search

Navigation