More accurate neuronavigation data provided by biomechanical modeling instead of rigid registration

Garlapati, Revanth Reddy; Roy, Aditi; Joldes, Grand Roman; Wittek, Adam; Mostayed, Ahmed; Doyle, Barry; Warfield, Simon K.; Kikinis, Ron; Knuckey, Neville W.; Bunt, Stuart; Miller, Karol

doi:10.3171/2013.12.jns131165

Cited by 35 publications

(42 citation statements)

References 34 publications

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“…We (and others) 3,5,8,9,11,16,18,20,23,24,28 have developed image-guidance updating frameworks formulated around computational model–based assimilation of brain shift measurement data acquired intraoperatively with minimally disruptive imaging tools. The accuracy and performance of these approaches have been evaluated in clinical cases, 16,21,24,28 but most analyses have been retrospective to surgery, and updated images have not routinely been generated in the OR.…”

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confidence: 99%

Intraoperative image updating for brain shift following dural opening

Fan¹,

Roberts

Schaewe

et al. 2016

JNS

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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.

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mentioning

confidence: 99%

Intraoperative image updating for brain shift following dural opening

Fan¹,

Roberts

Schaewe

et al. 2016

JNS

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“…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,

Roberts

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.

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“…77,82,83 In Wittek et al , 77 investigators demonstrate dramatic increases in computational speed for nonlinear models with results reflecting good deep structure alignment over several surgeries with a subsequent study showing similar results over many retrospective patient data sets. 23 …”

Section: Role Of Patient-specific Computational Modeling Towards Braimentioning

confidence: 99%

Computational Modeling for Enhancing Soft Tissue Image Guided Surgery: An Application in Neurosurgery

Miga

2015

Ann Biomed Eng

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With the recent advances in computing, the opportunities to translate computational models to more integrated roles in patient treatment are expanding at an exciting rate. One area of considerable development has been directed towards correcting soft tissue deformation within image guided neurosurgery applications. This review captures the efforts that have been undertaken towards enhancing neuronavigation by the integration of soft tissue biomechanical models, imaging and sensing technologies, and algorithmic developments. In addition, the review speaks to the evolving role of modeling frameworks within surgery and concludes with some future directions beyond neurosurgical applications.

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More accurate neuronavigation data provided by biomechanical modeling instead of rigid registration

Cited by 35 publications

References 34 publications

Intraoperative image updating for brain shift following dural opening

Intraoperative image updating for brain shift following dural opening

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

Computational Modeling for Enhancing Soft Tissue Image Guided Surgery: An Application in Neurosurgery

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