Novel method to improve feature extraction in MR for model-based image updating in image-guided neurosurgery

Kristen Chen; Chengpei Li; Xiaoyao Fan; Scott Davis; Linton Evans; Keith Paulsen

doi:10.1117/12.3006905

29 March 2024 Novel method to improve feature extraction in MR for model-based image updating in image-guided neurosurgery

Kristen Chen, Chengpei Li, Xiaoyao Fan, Scott Davis, Linton Evans, Keith Paulsen

Author Affiliations +

Proceedings Volume 12928, Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling; 1292828 (2024) https://doi.org/10.1117/12.3006905
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

In image-guided neurosurgery, preoperative magnetic resonance (pMR) images are rigidly registered with the patient’s head in the operating room. Image-guided systems incorporate this spatial information to provide real-time information on where surgical instruments are located with respect to preoperative imaging. The accuracy of these systems rely on the rigid relationship between the patient’s brain and the preoperative scan, which typically does not hold true due to intraoperative brain shift. To account for this brain shift, we previously developed an image-guidance updating framework that incorporates brain shift information acquired from registering intraoperative stereovision (iSV) surface with the pMR surface to create an updated magnetic resonance image (uMR). To register the iSV surface and the pMR surface, the two surfaces must have some matching features that can be used for registration. However, for some cases, the matching features could fall outside of the segmented brain volume causing a lack of matching features for registration between iSV and pMR surfaces. To capture features falling outside of the brain volume, we have developed a method to improve feature extraction, which involves performing a selective dilation in the region of the stereovision surface. The goal of this method is to capture features that fall outside of the brain volume without capturing too much noise. With further testing, this method has potential in supplementing brain segmentation to improve image registration between iSV and pMR surfaces within the image-guidance updating framework.

Citation Download Citation

Kristen Chen, Chengpei Li, Xiaoyao Fan, Scott Davis, Linton Evans, and Keith Paulsen "Novel method to improve feature extraction in MR for model-based image updating in image-guided neurosurgery", Proc. SPIE 12928, Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling, 1292828 (29 March 2024); https://doi.org/10.1117/12.3006905

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