Paper
17 March 2008 Mutual-information-corrected tumor displacement using intraoperative ultrasound for brain shift compensation in image-guided neurosurgery
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Abstract
Intraoperative ultrasound (iUS) has emerged as a practical neuronavigational tool for brain shift compensation in image-guided tumor resection surgeries. The use of iUS is optimized when coregistered with preoperative magnetic resonance images (pMR) of the patient's head. However, the fiducial-based registration alone does not necessarily optimize the alignment of internal anatomical structures deep in the brain (e.g., tumor) between iUS and pMR. In this paper, we investigated and evaluated an image-based re-registration scheme to maximize the normalized mutual information (nMI) between iUS and pMR to improve tumor boundary alignment using the fiducial registration as a starting point for optimization. We show that this scheme significantly (p<<0.001) reduces tumor boundary misalignment pre-durotomy. The same technique was employed to measure tumor displacement post-durotomy, and the locally measured tumor displacement was assimilated into a biomechanical model to estimate whole-brain deformation. Our results demonstrate that the nMI re-registration pre-durotomy is critical for obtaining accurate measurement of tumor displacement, which significantly improved model response at the craniotomy when compared with stereopsis data acquired independently from the tumor registration. This automatic and computationally efficient (<2min) re-registration technique is feasible for routine clinical use in the operating room (OR).
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Songbai Ji, Alex Hartov, David Roberts M.D., and Keith Paulsen "Mutual-information-corrected tumor displacement using intraoperative ultrasound for brain shift compensation in image-guided neurosurgery", Proc. SPIE 6918, Medical Imaging 2008: Visualization, Image-Guided Procedures, and Modeling, 69182H (17 March 2008); https://doi.org/10.1117/12.770363
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Cited by 6 scholarly publications and 2 patents.
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KEYWORDS
Tumors

Data modeling

Image registration

Brain

Image segmentation

Neuroimaging

Surgery

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