Estimation of Intraoperative Brain Deformation

Schaewe

et al. 2016

JNS

Self Cite

OBJECTIVE Preoperative 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. METHODS In 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. RESULTS The 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. CONCLUSIONS This 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.

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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Intraoperative image updating for brain shift following dural opening

Fan¹,

Schaewe

et al. 2016

JNS

Self Cite

“…Various techniques have been developed to account for the brain deformation resulting from surgical events such as dural opening, resection, and retraction. 3,5,7,10,14,15,21,25–27 Nevertheless, all of these compensation methods depend on an initial alignment achieved by patient registration; hence, the registration accuracy attained later in a case is directly influenced by the accuracy of the initial patient registration.…”

Section: Discussionmentioning

confidence: 99%

Intraoperative fiducial-less patient registration using volumetric 3D ultrasound: a prospective series of 32 neurosurgical cases

Fan¹,

et al. 2015

JNS

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Object Fiducial-based registration (FBR) is used widely for patient registration in image-guided neurosurgery. The authors of this study have developed an automatic fiducial-less registration (FLR) technique to find the patient-to-image transformation by directly registering 3D ultrasound (3DUS) with MR images without incorporating prior information. The purpose of the study was to evaluate the performance of the FLR technique when used prospectively in the operating room and to compare it with conventional FBR. Methods In 32 surgical patients who underwent conventional FBR, preoperative T1-weighted MR images (pMR) with attached fiducial markers were acquired prior to surgery. After craniotomy but before dural opening, a set of 3DUS images of the brain volume was acquired. A 2-step registration process was executed immediately after image acquisition: 1) the cortical surfaces from pMR and 3DUS were segmented, and a multistart sum-of-squared-intensity-difference registration was executed to find an initial alignment between down-sampled binary pMR and 3DUS volumes; and 2) the alignment was further refined by a mutual information-based registration between full-resolution grayscale pMR and 3DUS images, and a patient-to-image transformation was subsequently extracted. Results To assess the accuracy of the FLR technique, the following were quantified: 1) the fiducial distance error (FDE); and 2) the target registration error (TRE) at anterior commissure and posterior commissure locations; these were compared with conventional FBR. The results showed that although the average FDE (6.42 ± 2.05 mm) was higher than the fiducial registration error (FRE) from FBR (3.42 ± 1.37 mm), the overall TRE of FLR (2.51 ± 0.93 mm) was lower than that of FBR (5.48 ± 1.81 mm). The results agreed with the intent of the 2 registration techniques: FBR is designed to minimize the FRE, whereas FLR is designed to optimize feature alignment and hence minimize TRE. The overall computational cost of FLR was approximately 4–5 minutes and minimal user interaction was required. Conclusions Because the FLR method directly registers 3DUS with MR by matching internal image features, it proved to be more accurate than FBR in terms of TRE in the 32 patients evaluated in this study. The overall efficiency of FLR in terms of the time and personnel involved is also improved relative to FBR in the operating room, and the method does not require additional image scans immediately prior to surgery. The performance of FLR and these results suggest potential for broad clinical application.

“…15 Thus, better strategies may exist such as those that incorporate intramodality image-based registration as displacement mapping methods which provide data for model-based image compensation approaches. 34 The relative spatial similarity in the registration transformations generated with MI and CR was also investigated. In the ten patients evaluated, we found that the average distance between converged 3DUS voxel locations produced by the two methods was 2.6 mm.…”

Section: Discussionmentioning

confidence: 99%

Intraoperative patient registration using volumetric true 3D ultrasound without fiducials

Hartov

et al. 2012

Medical Physics

Self Cite

Because common features deep in the brain and throughout the surgical volume of interest are used, intraoperative fiducial-less patient registration is possible on-demand, which is attractive in cases where preoperative patient registration is compromised (e.g., from loss∕movement of skin-affixed fiducials) or not possible (e.g., in cases of emergency when external fiducials were not placed in time). CR registration was more robust than MI (capture range about twice as big) and appears to be more accurate, although both methods are comparable to or better than fiducial-based registration in the patient cases evaluated. The results presented here suggest that 3DUS image-based patient registration holds promise for clinical application in the future.