Framework for a Low-Cost Intra-Operative Image-Guided Neuronavigator Including Brain Shift Compensation

Bucki, Marek; Lobos, Claudio

doi:10.1109/iembs.2007.4352429

“…A possible enrichment relies on functions that incorporate the radial and angular behavior of the asymptotic linear-elastic crack-tip displacement field, which is two-dimensional by nature, i.e. (6) where r and are the local polar coordinates of the position of a point x measured in the (e s (x tip ), e n (x tip ))-axes, as shown in Fig. 1(b) [9].…”

Section: Basic Principles Of Fem and Xfemmentioning

confidence: 99%

“…Resection, i.e. the removal of tissues, has been modeled by deleting FEs that were tagged before surgery [26], or that were falling within the resection cavity visible on the intraoperative image [6,15].…”

Section: Introductionmentioning

confidence: 99%

2D XFEM-based modeling of retraction and successive resections for preoperative image update

Vigneron¹,

Duflot²,

Robe³

et al. 2009

Comp. Aided Surgery

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We consider the problem of improving outcomes for neurosurgery patients by enhancing intraoperative navigation and guidance. Currently intraoperative navigation systems do not accurately account for brain shift or for tissue resection. In this paper, we describe how preoperative images are incrementally updated to take into account any type of brain tissue deformation that can occur during surgery, and thus to improve the accuracy of image-guided navigation systems. For this purpose, we develop a nonrigid image registration technique using on a biomechanical model, which deforms based on the Finite Element Method (FEM). While FEM has been successfully used for dealing with deformation such as brain shift, FEM has difficulties dealing with tissue discontinuities. Here, we describe the novel application of the eXtended Finite Element Method (XFEM) in the field of image-guided surgery, in order to model brain deformations that imply tissue discontinuities. In particular, this paper presents a detailed account of the use of XFEM for dealing with retraction and successive resections, and demonstrates the feasibility of the approach by considering 2D examples based on intraoperative MR images. For evaluating our results, we compute the modified Hausdorff distance between Canny edges extracted from images before and after registration. We show that this distance decreases after registration, and thus demonstrate that our approach improves alignment of intraoperative images.

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“…Arguing against the high cost of the intra-operative MRI imaging devices, some authors have proposed to couple the biomechanical model of the brain with low-cost readily available intra-operative data such as laser-range scanner systems [Audette et al , 2003] [Miga et al , 2003] or intra-operative ultrasound [Comeau et al , 2000][ Bucki et al , 2007] [Reinertsen et al , 2007]. These techniques give a crucial and very central position to the patient biomechanical brain model.…”

Section: Introductionmentioning

confidence: 99%

In vivo measurement of human brain elasticity using a light aspiration device

Schiavone

¹

,

Chassat

²

,

Boudou

³

et al. 2009

Medical Image Analysis

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The brain deformation that occurs during neurosurgery is a serious issue impacting the patient "safety" as well as the invasiveness of the brain surgery. Model-driven compensation is a realistic and efficient solution to solve this problem. However, a vital issue is the lack of reliable and easily obtainable patient-specific mechanical characteristics of the brain which, according to clinicians' experience, can vary considerably. We designed an aspiration device that is able to meet the very rigorous sterilization and handling process imposed during surgery, and especially neurosurgery. The device, which has no electronic component, is simple, light and can be considered as an ancillary instrument. The deformation of the aspirated tissue is imaged via a mirror using an external camera. This paper describes the experimental setup as well as its use during a specific neurosurgery. The experimental data was used to calibrate a continuous model. We show that we were able to extract an in vivo constitutive law of the brain elasticity: thus for the first time, measurements are carried out per-operatively on the patient, just before the resection of the brain parenchyma. This paper discloses the results of a difficult experiment and provide for the first time in vivo data on human brain elasticity. The results point out the softness as well as the highly non-linear behavior of the brain tissue.

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“…Resection, i.e. the removal of tissues, has been modeled by deleting FEs that were tagged before surgery [26], or that were falling within the resection cavity visible on the intraoperative image [6,15]. …”

Section: Introductionmentioning

confidence: 99%

2D XFEM-based modeling of retraction and successive resections for preoperative image update

Vigneron

¹

,

Duflot

²

,

Robe

³

et al. 2009

Computer Aided Surgery

View full text Add to dashboard Cite

We consider the problem of improving outcomes for neurosurgery patients by enhancing intraoperative navigation and guidance. Currently intraoperative navigation systems do not accurately account for brain shift or for tissue resection. In this paper, we describe how preoperative images are incrementally updated to take into account any type of brain tissue deformation that can occur during surgery, and thus to improve the accuracy of image-guided navigation systems. For this purpose, we develop a nonrigid image registration technique using on a biomechanical model, which deforms based on the Finite Element Method (FEM). While FEM has been successfully used for dealing with deformation such as brain shift, FEM has difficulties dealing with tissue discontinuities. Here, we describe the novel application of the eXtended Finite Element Method (XFEM) in the field of image-guided surgery, in order to model brain deformations that imply tissue discontinuities. In particular, this paper presents a detailed account of the use of XFEM for dealing with retraction and successive resections, and demonstrates the feasibility of the approach by considering 2D examples based on intraoperative MR images. For evaluating our results, we compute the modified Hausdorff distance between Canny edges extracted from images before and after registration. We show that this distance decreases after registration, and thus demonstrate that our approach improves alignment of intraoperative images.

show abstract

Framework for a Low-Cost Intra-Operative Image-Guided Neuronavigator Including Brain Shift Compensation

Cited by 12 publications

References 23 publications

2D XFEM-based modeling of retraction and successive resections for preoperative image update

2D XFEM-based modeling of retraction and successive resections for preoperative image update

In vivo measurement of human brain elasticity using a light aspiration device

2D XFEM-based modeling of retraction and successive resections for preoperative image update

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