Brain Shift Correction Based on a Boundary Element Biomechanical Model with Different Material Properties

Image Updating for Brain Shift Compensation During Resection

Operative Neurosurgery ◽

10.1093/ons/opx123 ◽

2017 ◽

Vol 14 (4) ◽

pp. 402-411 ◽

Cited By ~ 5

Author(s):

Xiaoyao Fan ◽

David W Roberts ◽

Jonathan D Olson ◽

Songbai Ji ◽

Timothy J Schaewe ◽

...

Keyword(s):

Biomechanical Model ◽

Partial Resection ◽

Computational Time ◽

Brain Shift ◽

Registration Errors ◽

Brain Deformation ◽

Preoperative Magnetic Resonance ◽

Surgical Field ◽

Image Pairs ◽

Dural Opening

Abstract BACKGROUND In open-cranial neurosurgery, preoperative magnetic resonance (pMR) images are typically coregistered for intraoperative guidance. Their accuracy can be significantly degraded by intraoperative brain deformation, especially when resection is involved. OBJECTIVE To produce model updated MR (uMR) images to compensate for brain shift that occurred during resection, and evaluate the performance of the image-updating process in terms of accuracy and computational efficiency. METHODS In 14 resection cases, intraoperative stereovision image pairs were acquired after dural opening and during resection to generate displacement maps of the surgical field. These data were assimilated by a biomechanical model to create uMR volumes of the evolving surgical field. A tracked stylus provided independent measurements of feature locations to quantify target registration errors (TREs) in the original coregistered pMR and uMR as surgery progressed. RESULTS Updated MR TREs were 1.66 ± 0.27 and 1.92 ± 0.49 mm in the 14 cases after dural opening and after partial resection, respectively, compared to 8.48 ± 3.74 and 8.77 ± 4.61 mm for pMR, respectively. The overall computational time for generating uMRs after partial resection was less than 10 min. CONCLUSION We have developed an image-updating system to compensate for brain deformation during resection using a computational model with data assimilation of displacements measured with intraoperative stereovision imaging that maintains TREs less than 2 mm on average.

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Intraoperative image updating for brain shift following dural opening

Journal of Neurosurgery ◽

10.3171/2016.6.jns152953 ◽

2016 ◽

Vol 126 (6) ◽

pp. 1924-1933 ◽

Cited By ~ 14

Author(s):

Xiaoyao Fan ◽

David W. Roberts ◽

Timothy J. Schaewe ◽

Songbai Ji ◽

Leslie H. Holton ◽

...

Keyword(s):

Biomechanical Model ◽

Magnetic Resonance Images ◽

Computational Time ◽

Average Error ◽

Cortical Surface ◽

Brain Shift ◽

Surgical Workflow ◽

Brain Deformation ◽

Time Required ◽

Dural Opening

OBJECTIVEPreoperative magnetic resonance images (pMR) are typically coregistered to provide intraoperative navigation, the accuracy of which can be significantly compromised by brain deformation. In this study, the authors generated updated MR images (uMR) in the operating room (OR) to compensate for brain shift due to dural opening, and evaluated the accuracy and computational efficiency of the process.METHODSIn 20 open cranial neurosurgical cases, a pair of intraoperative stereovision (iSV) images was acquired after dural opening to reconstruct a 3D profile of the exposed cortical surface. The iSV surface was registered with pMR to detect cortical displacements that were assimilated by a biomechanical model to estimate whole-brain nonrigid deformation and produce uMR in the OR. The uMR views were displayed on a commercial navigation system and compared side by side with the corresponding coregistered pMR. A tracked stylus was used to acquire coordinate locations of features on the cortical surface that served as independent positions for calculating target registration errors (TREs) for the coregistered uMR and pMR image volumes.RESULTSThe uMR views were visually more accurate and well aligned with the iSV surface in terms of both geometry and texture compared with pMR where misalignment was evident. The average misfit between model estimates and measured displacements was 1.80 ± 0.35 mm, compared with the average initial misfit of 7.10 ± 2.78 mm between iSV and pMR, and the average TRE was 1.60 ± 0.43 mm across the 20 patients in the uMR image volume, compared with 7.31 ± 2.82 mm on average in the pMR cases. The iSV also proved to be accurate with an average error of 1.20 ± 0.37 mm. The overall computational time required to generate the uMR views was 7–8 minutes.CONCLUSIONSThis study compensated for brain deformation caused by intraoperative dural opening using computational model–based assimilation of iSV cortical surface displacements. The uMR proved to be more accurate in terms of model-data misfit and TRE in the 20 patient cases evaluated relative to pMR. The computational time was acceptable (7–8 minutes) and the process caused minimal interruption of surgical workflow.

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