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SPL brain tumor resection image datasets

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Resource Type

  1. Software


  1. Resource Description
    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.
  2. Used by
    Surgical Planning Laboratory (BWH)
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The eagle-i Consortium is supported by NIH Grant #5U24RR029825-02 / Copyright 2016