Efficient Variational Approach to Multimodal Registration of Anatomical and Functional Intra-Patient Tumorous Brain Data
Abstract
This paper addresses the functional localization of intra-patient images of the brain. Functional images of the brain (fMRI and PET) provide information about brain function and metabolism whereas anatomical images (MRI and CT) supply the localization of structures with high spatial resolution. The goal is to find the geometric correspondence between functional and anatomical images in order to complement and fuse the information provided by each imaging modality. The proposed approach is based on a variational formulation of the image registration problem in the frequency domain. It has been implemented as a C/C library which is invoked from a GUI. This interface is routinely used in the clinical setting by physicians for research purposes (Inscanner, Alicante, Spain), and may be used as well for diagnosis and surgical planning. The registration of anatomic and functional intra-patient images of the brain makes it possible to obtain a geometric correspondence which allows for the localization of the functional processes that occur in the brain. Through 18 clinical experiments, it has been demonstrated how the proposed approach outperforms popular state-of-the-art registration methods in terms of efficiency, information theory-based measures (such as mutual information) and actual registration error (distance in space of corresponding landmarks).
References
- 1. , Image registration methods: A survey, Image Vis. Comput. 21(11) (2003) 977–1000. Crossref, ISI, Google Scholar
- 2. , Deformable medical image registration: A survey, IEEE Trans. Med. Imag. 32(7) (2013) 1153–1190. Crossref, Medline, ISI, Google Scholar
- 3. ,
Graph-based deformable image registration , Handbook of Biomedical Imaging; Methodologies and Clinical Research (Springer, 2015). Crossref, Google Scholar - 4. , Brain functional localization: A survey of image registration techniques, IEEE Trans. Image Process. 26(4) (2007) 427–451. Crossref, Google Scholar
- 5. , Localization of epileptic foci using multimodality neuroimaging, Int. J. Neural Syst. 23(1) (2013) 1230001. Link, ISI, Google Scholar
- 6. , PET/SPECT molecular imaging in clinical neuroscience: Recent advances in the investigation of CNS diseases, Quant. Imaging Med. Surg. 5(3) (2015) 433–447. Medline, Google Scholar
- 7. , Update on time-of-flight PET imaging, J. Nucl. Med. 56(1) (2015) 98–105. Crossref, Medline, ISI, Google Scholar
- 8. , Adaptive filtering and random variables coefficients for analyzing functional magnetic resonance imaging data, Int. J. Neural Syst. 23(3) (2013) 1350011. Link, ISI, Google Scholar
- 9. , Singular spectrum analysis and adaptive filtering enhance the functional connectivity analysis of resting state FMRI data, Int. J. Neural Syst. 24(3) (2014) 1450010. Link, ISI, Google Scholar
- 10. , Advantages in functional imaging of the brain, Front. Hum. Neurosci. 9(249) (2015) 1–6. Medline, Google Scholar
- 11. , Neurophysiological investigation of the basis of the fMRI signal, Nature 412 (2001) 150–157. Crossref, Medline, ISI, Google Scholar
- 12. , Vision 20/20: Perspectives on automated image segmentation for radiotherapy, Med. Phys. 41(5) (2014) 13 pp. Crossref, ISI, Google Scholar
- 13. , Self-supervised MRI tissue segmentation by discriminative clustering, Int. J. Neural Syst. 24(1) (2014) 1450004. Link, ISI, Google Scholar
- 14. , Ten years of progress in radiation oncology, BMC Cancer 11(503) (2011) 4 pp. Medline, Google Scholar
- 15. , The ITK Software Guide, 2nd edn. (Kitware, Clifton Park, NY, 2005). Google Scholar
- 16. , Elastix: A toolbox for intensity-based medical image registration, IEEE Trans. Med. Imaging 29(1) (2010) 196–205. Crossref, Medline, ISI, Google Scholar
- 17. , A unified approach to fast image registration and a new curvature based registration technique, Linear Algebra Appl. 308 (2004) 107–124. Crossref, Google Scholar
- 18. , A generic framework for modeling brain deformation as a constrained parametric optimization problem to aid non-diffeomorphic image registration inbrain tumor imaging, Methods Inf. Med. 51(5) (2012) 429–440. Crossref, Medline, ISI, Google Scholar
- 19. , A Fourier domain framework for variational image registration, J. Math. Imaging Vis. 32(1) (2008) 57–72. Crossref, ISI, Google Scholar
- 20. , The correlation ratio as a new similarity measure for multimodal image registration, in Proc. MICCAI’98,
LNCS , Vol. 1496 (1998), pp. 1115–1124. Google Scholar - 21. , Numerical Methods for Image Registration (Oxford University Press, NY, 2004). Google Scholar
- 22. , Frequency implementation of the Euler-Lagrange equations for variational image registration, Signal Process. Lett. 15 (2008) 321–324. Crossref, ISI, Google Scholar
- 23. , Evaluation of registration methods on thoracic CT: The empire10 challenge, IEEE Trans. Med. Imaging 30(11) (2011) 1901–1920. Crossref, Medline, ISI, Google Scholar
- 24. , Comparative evaluation of registration algorithms in different brain databases with varying difficulty: Results and insights, IEEE Trans. Med. Imaging 33(10) (2014) 2039–2065. Crossref, Medline, ISI, Google Scholar
- 25. ,
Optimal parameters selection for non-parametric image registration methods , in Advanced Concepts for Intelligent Vision SystemsLecture Notes in Computer Science , Vol. 4179 (Springer, 2006), pp. 564–575. Crossref, Google Scholar - 26. G. Hermosillo, C. Chefd’Hotel and O. Faugeras, A variational approach to multi-modal image matching, Technical Report 4117 (INRIA, 2001). Google Scholar
| Remember to check out the Most Cited Articles! |
|---|
|
Check out our titles in neural networks today! |


