An integrated range-sensing, segmentation and registration framework for the characterization of intra-surgical brain deformations in image-guided surgery
✍ Scribed by Michel A Audette; Kaleem Siddiqi; Frank P Ferrie; Terry M Peters
- Publisher
- Elsevier Science
- Year
- 2003
- Tongue
- English
- Weight
- 918 KB
- Volume
- 89
- Category
- Article
- ISSN
- 1077-3142
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✦ Synopsis
Image-guided surgery (IGS) is a technique for localizing anatomical structures on the basis of volumetric image data and for determining the optimal surgical path to reach these structures, by the means of a localization device, or probe, whose position is tracked over time. The usefulness of this technology hinges on the accuracy of the transformation between the image volume and the space occupied by the patient anatomy and spanned by the probe. Unfortunately, in neurosurgery this transformation can be degraded by intra-surgical brain shift, which often measures more than 10 mm and can exceed 25 mm. We propose a method for characterizing brain shift that is based on non-rigid surface registration, and can be combined with a constitutively realistic finite element approach for volumetric displacement estimation. The proposed registration method integrates in a unified framework all of the stages required to estimate the movement of the cortical surface in the operating room: model-based segmentation of the pre-operative brain surface in magnetic resonance image data, range-sensing of the cortex in the OR, range-MR rigid transformation computation, and range-based non-rigid brain motion estimation. The brain segmentation technique is an adaptation of the surface evolution model. Its convergence to the brain boundary is the result of a speed term restricted