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

• Accurate localization of implanted intra-cranial electroencephalography electrodes in epilepsy patients
• A framework for training a statistical model of intra-subject non-rigid deformations induced by surgical procedures
• The statistical deformation model captures the main modes of deformation from clinical pre-op and post-op training images
• Apply our learned statistical deformation model to directly co-register post-operative CT to pre-operative MRI
• Significant improvement in registration performance, with increased robustness and reduced worst-case performance

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.

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