SIFT-based cortical surface shift estimation for intraoperative image updating

Xiaoyao Fan; Kristen L. Chen; William R. Warner; Ryan B. Duke; Chen Li; Linton T. Evans; Keith D. Paulsen

doi:10.1117/12.2654258

3 April 2023 SIFT-based cortical surface shift estimation for intraoperative image updating

Xiaoyao Fan, Kristen L. Chen, William R. Warner, Ryan B. Duke, Chen Li, Linton T. Evans, Keith D. Paulsen

Proceedings Volume 12466, Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling; 1246613 (2023) https://doi.org/10.1117/12.2654258
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

In open cranial procedures, the accuracy of image guidance using preoperative MR (pMR) images can be degraded by intraoperative brain deformation. Intraoperative stereovision (iSV) has been used to acquire 3D surface profile of the exposed cortex at different surgical stages, and surface displacements can be extracted to drive a biomechanical model as sparse data to provide updated MR (uMR) images that match the surgical scene. In previous studies, we have employed an Optical Flow (OF) based registration technique to register iSV surfaces acquired from different surgical stages and estimate cortical surface shift throughout surgery. The technique was efficient and accurate but required manually selected Regions of Interest (ROI) in each image after resection began. In this study, we present a registration technique based on Scale Invariant Feature Transform (SIFT) algorithm and illustrate the methods using an example patient case. Stereovision images of the cortical surface were acquired and reconstructed at different time points during surgery. Both SIFT and OF based registration techniques were used to estimate cortical shift, and extracted displacements were compared against ground truth data. Results show that the overall errors of SIFT and OF based techniques were 0.65±0.53 mm and 2.18±1.35 mm in magnitude, respectively, on the intact cortical surface. The OF-based technique generated inaccurate sparse data near the resection cavity region, whereas SIFT-based technique only generated accurate sparse data. The computational efficiency was ⪅0.5 s and ⪆20 s for SIFT and OF based techniques, respectively. Thus, the SIFT-based registration technique shows promise for OR applications.

Conference Presentation

Citation Download Citation

Xiaoyao Fan, Kristen L. Chen, William R. Warner, Ryan B. Duke, Chen Li, Linton T. Evans, and Keith D. Paulsen "SIFT-based cortical surface shift estimation for intraoperative image updating", Proc. SPIE 12466, Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling, 1246613 (3 April 2023); https://doi.org/10.1117/12.2654258

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