12 March 2014 Intraoperative measurement of indenter-induced brain deformation: a feasibility study
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Abstract
Accurate measurement of soft tissue material properties is critical for characterizing its biomechanical behaviors but can be challenging especially for the human brain in vivo. In this study, we investigated the feasibility of inducing and detecting cortical surface deformation intraoperatively for patients undergoing open skull neurosurgeries. A custom diskshaped indenter made of high-density tungsten (diameter of 15 mm with a thickness of 6 mm) was used to induce deformation on the brain cortical surface immediately after dural opening. Before and after placing the indenter, sequences (typically 250 frames at 15 frames-per-second, or ~17 seconds) of high-resolution stereo image pairs were acquired to capture the harmonic motion of the exposed cortical surface as due to blood pressure pulsation and respiration. For each sequence with the first left image serving as a baseline, an optical-flow motion-tracking algorithm was used to detect in-sequence cortical surface deformation. The resulting displacements of the exposed features within the craniotomy were spatially averaged to identify the temporal frames corresponding to motion peak magnitudes. Corresponding image pairs were then selected to reconstruct full-field three-dimensional (3D) cortical surfaces before and after indentation, respectively, from which full 3D displacement fields were obtained by registering their projection images. With one clinical patient case, we illustrate the feasibility of the technique in detecting indenter-induced cortical surface deformation in order to allow subsequent processing to determine material properties of the brain in vivo.
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Songbai Ji, Songbai Ji, Xiaoyao Fan, Xiaoyao Fan, David W. Roberts, David W. Roberts, Keith D. Paulsen, Keith D. Paulsen, } "Intraoperative measurement of indenter-induced brain deformation: a feasibility study", Proc. SPIE 9036, Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling, 903616 (12 March 2014); doi: 10.1117/12.2043706; https://doi.org/10.1117/12.2043706
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