Deformable registration of preoperative MR, pre-resection ultrasound, and post-resection ultrasound images of neurosurgery

Rivaz, Hassan; Collins, D. Louis

doi:10.1007/s11548-014-1099-4

Cited by 35 publications

(26 citation statements)

References 41 publications

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“…The order of magnitude of the remaining shit is similar to the ones obtained just after dura opening. 3,5,18 During resection, these results can therefore be considered very satisfying. This can be confirmed qualitatively, as shown in Figures 1 and 2.…”

Section: Surface Tumor Cases 1-3mentioning

confidence: 94%

“…In addition to providing sub-surface data, this system is in widespread use, portable, cost-effective and do not require major changes in the surgical procedure. On one hand, image-based methods can be used to register pMR and iUS acquired during or after resection, using generated peudo-US images, 17 LC metrics 6 or patch-based correlation ratio, 18 with promising or less conclusive results. 19 On the other hand, iUS were used with a model-based method, 20 with the neutralization of mechanical elements inside the resection cavity.…”

Section: Introductionmentioning

confidence: 99%

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Resection-induced brain-shift compensation using vessel-based methods

Morin

Courtecuisse

Reinertsen

et al. 2018

Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling

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Most brain-shift compensation methods address the problem of updating preoperative images to reflect brain deformations following the craniotomy and dura opening. However, fewer enable to take into account the resection-induced deformations occuring all along the tumor removal procedure. This paper evaluates the use of two existing methods to tackle that problem. Both techniques rely on blood vessels segmented then skeletonized from preoperative MR Angiography and navigated Doppler Ultrasound images acquired during resection. While the first one proposes to register the vascular trees using a rigid modified ICP algorithm, the second method relies on a non-rigid constrained-based biomechanical approach. Quantitative results are provided, based on distances between paired landmarks set on blood vessels then anatomical structures delineated on medical images. A qualitative evaluation of the compensation is also presented using initial and updated images. An analysis on three cases of surface tumor shows both methods, especially the biomechanical one, can compensate up to 63% of the brain-shift, with an error in the range of 2 mm. However, these results are not reproduced on a more complex case of deep tumor. While more patients must be included, these preliminary results show that vesselbased methods are well suited to compensate for resection-induced brain-shift, but better outcomes in complex cases still require to improve the methods to take the resection into account.

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Section: Surface Tumor Cases 1-3mentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Resection-induced brain-shift compensation using vessel-based methods

Morin

Courtecuisse

Reinertsen

et al. 2018

Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling

View full text Add to dashboard Cite

show abstract

“…On the one hand, deformations of soft brain tissues can be visualized using B-mode US imaging. These images can be registered with preoperative MRI using mutual information (Ji et al, 2008), cross correlation (Rivaz & Collins, 2015) or linear correlation of linear combination (Fuerst et al, 2014). Finally, Mercier et al (2012) generated pseudo US images from preoperative MRI, then registered them with the real intraoperative US images.…”

Section: Brain-shift Compensation Methods For Brain Tumor Surgerymentioning

confidence: 99%

“…All of them rely on the acquisition of data during the procedure, providing information about the current tissue deformations. While Magnetic Resonance Imaging (MRI) is the reference preoperative exam, various intraoperative imaging systems are used in the literature such as MRI (Clatz et al, 2005;Vigneron et al, 2012), laser range scanners (LRS) (Sun et al, 2014;Miga et al, 2015), stereo cameras (Sun et al, 2005a) or ultrasound (US) imaging (Reinertsen et al, 2014;Rivaz & Collins, 2015). However, the data obtained cannot be directly used for surgical navigation: US images are of poor quality compared to preoperative MRI, and surface data alone are not clinically relevant.…”

Section: Introductionmentioning

confidence: 99%

Brain-shift compensation using intraoperative ultrasound and constraint-based biomechanical simulation

Morin

Courtecuisse

Reinertsen

et al. 2017

Medical Image Analysis

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“…On the one hand, brain soft tissues can be visualized using intraoperative B-mode imaging. Pureimage based registration methods are then proposed in the literature to register these images with the MRI acquired prior to surgery (Mercier et al, 2012;Fuerst et al, 2014;Rivaz and Collins, 2015).…”

Section: Relative Workmentioning

confidence: 99%

Biomechanical Modeling of Brain Soft Tissues for Medical Applications

Morin¹,

Chabanas²,

Courtecuisse³

2017

Biomechanics of Living Organs

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For more than 60 years, many works have focused on the determination of the brain biomechanical properties. While the highly non linear behavior of the organ as well as the very low soft tissues stiness are stressed out, no consensus is universally accepted. Variations in the reported constitutive laws and parameters may be due to the diversity of the experimental protocols and to patient peculiarities. In addition to these rheological properties, boundary conditions are at least as important. Especially, a low sensitivity of the model to these properties is observed when loads are imposed through displacements. For this reason and/or due to the small displacements observed and execution time requirements, most nite element brain models are simulated based on linear elastic law. Nevertheless, models with various boundary conditions have been proposed in the literature, being carefully designed for specic medical applications. A survey about brain soft tissues biomechanical modeling is presented in this paper. The main works are then presented before describing a new vessel-based brain-shift compensation model using intra-operative Doppler ultrasound imaging. Keywords Brain • Finite element modeling • Biomechanical properties • Constitutive laws • Boundary conditions • Medical simulation.

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Deformable registration of preoperative MR, pre-resection ultrasound, and post-resection ultrasound images of neurosurgery

Cited by 35 publications

References 41 publications

Resection-induced brain-shift compensation using vessel-based methods

Resection-induced brain-shift compensation using vessel-based methods

Brain-shift compensation using intraoperative ultrasound and constraint-based biomechanical simulation

Biomechanical Modeling of Brain Soft Tissues for Medical Applications

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