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Neuroimage Clin. 2015 Dec 10;10:291-301. doi: 10.1016/j.nicl.2015.12.001. eCollection 2016.

Learning intervention-induced deformations for non-rigid MR-CT registration and electrode localization in epilepsy patients.

Author information

1
Department of Radiology & Biomedical Imaging, Yale University, New Haven, CT, USA.
2
Department of Radiology & Biomedical Imaging, Yale University, New Haven, CT, USA; Department of Electrical Engineering, Yale University, New Haven, CT, USA; Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
3
Department of Radiology & Biomedical Imaging, Yale University, New Haven, CT, USA; Department of Biomedical Engineering, Yale University, New Haven, CT, USA.

Abstract

This paper describes a framework for learning a statistical model of non-rigid deformations induced by interventional procedures. We make use of this learned model to perform constrained non-rigid registration of pre-procedural and post-procedural imaging. We demonstrate results applying this framework to non-rigidly register post-surgical computed tomography (CT) brain images to pre-surgical magnetic resonance images (MRIs) of epilepsy patients who had intra-cranial electroencephalography electrodes surgically implanted. Deformations caused by this surgical procedure, imaging artifacts caused by the electrodes, and the use of multi-modal imaging data make non-rigid registration challenging. Our results show that the use of our proposed framework to constrain the non-rigid registration process results in significantly improved and more robust registration performance compared to using standard rigid and non-rigid registration methods.

KEYWORDS:

Computed tomography (CT); Epilepsy; Magnetic resonance imaging (MRI); Non-rigid registration; Statistical deformation model; Surgical navigation

PMID:
26900569
PMCID:
PMC4724039
DOI:
10.1016/j.nicl.2015.12.001
[Indexed for MEDLINE]
Free PMC Article
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