Surgical Planning Laboratory - Brigham & Women's Hospital - Boston, Massachusetts USA - a teaching affiliate of Harvard Medical School

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Volumetric Non-Rigid Registration for MRI-guided Brain Tumor Surgery

Surgical Planning Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Publication Date:
SPL 2007 Aug;
Appears in Collections:
SPL, Download Data, NAC, NCIGT, SLICER
U41 RR019703/RR/NCRR NIH HHS/United States
P41 RR013218/RR/NCRR NIH HHS/United States
P01 CA067165/CA/NCI NIH HHS/United States
R03 CA126466/CA/NCI NIH HHS/United States
Generated Citation:
Talos I-F., Archip N. Volumetric Non-Rigid Registration for MRI-guided Brain Tumor Surgery. SPL 2007 Aug;
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The accuracy of neurosurgical navigation systems is seriously compromised by brain shift, i.e. changes in the spatial position of the lesion and surrounding brain tissue, which inevitably occur during the surgical procedure, in response to surgical manipulation (resection, retraction, CSF leakage) and administration of anesthetic drugs. These changes in brain spatial configuration, summarized under the generic term of brain shift, occur according to a non-linear pattern and lead to significant mis-registration between pre-operative image data (MR, CT) and the intraoperative brain configuration. Non-rigid registration techniques are increasingly being employed to maintain an accurate alignment between pre-operative and intra-operative images. These techniques provide the ability to estimate transformations that model not only affine parameters (global translation, rotation, scale and shear), but also local deformations. Higher-order transformation models, with increased number of parameters and significant computing capabilities are usually required for this purpose. Our group was the first to demonstrate the feasibility of a non-rigid registration approach capable to compensate for the volumetric brain deformations within the time constraints imposed by neurosurgery (Archip et al., 2007). Augmented reality visualizations of functionally eloquent brain structures (based on pre-operative anatomic, functional and Diffusion Tensor MRI, non-rigidly registered with intra-operative MR-image updates) were presented to the surgeon during brain tumor resections in near real-time. We are pleased to make available the image datasets used in our study, results of our algorithms, and open source software (3D Slicer) for data access and processing to interested parties, as a free service.

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