A surface registration method for quantification of intraoperative brain deformations in image-guided neurosurgery

Paul, Perrine; Morandi, Xavier; Jannin, Pierre

doi:10.1109/titb.2009.2025373

Cited by 37 publications

(44 citation statements)

References 32 publications

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“…With respect to sparse surface data digitization for IGLS, registration has been performed using manual swabbing with a tracked probe [14], laser-range scanning [5], ultrasound [1], [15], [16], time of flight imaging [17], stereoscopic imaging [18], [19], and conoscopic holographic surface scanning [20]. Recently, we did a comprehensive study comparing registration results using swabbing, laser range scanning, and conoscopic holographic scanning in [21].…”

Section: Introductionmentioning

confidence: 99%

Improving Registration Robustness for Image-Guided Liver Surgery in a Novel Human-to-Phantom Data Framework

Collins

Weis

Heiselman

et al. 2017

IEEE Trans. Med. Imaging

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In open image-guided liver surgery (IGLS), a sparse representation of the intraoperative organ surface can be acquired to drive image-to-physical registration. We hypothesize that uncharacterized error induced by variation in the collection patterns of organ surface data limits the accuracy and robustness of an IGLS registration. Clinical validation of such registration methods is challenged due to the difficulty in obtaining data representative of the true state of organ deformation. We propose a novel human-to-phantom validation framework that transforms surface collection patterns from in vivo IGLS procedures (n = 13) onto a well-characterized hepatic deformation phantom for the purpose of validating surface-driven, volumetric nonrigid registration methods. An important feature of the approach is that it centers on combining workflow-realistic data acquisition and surgical deformations that are appropriate in behavior and magnitude. Using the approach, we investigate volumetric target registration error (TRE) with both current rigid IGLS and our improved nonrigid registration methods. Additionally, we introduce a spatial data resampling approach to mitigate the workflow-sensitive sampling problem. Using our human-to-phantom approach, TRE after routine rigid registration was 10.9 ± 0.6 mm with a signed closest point distance associated with residual surface fit in the range of ±10 mm, highly representative of open liver resections. After applying our novel resampling strategy and improved deformation correction method, TRE was reduced by 51%, i.e., a TRE of 5.3 ± 0.5 mm. This paper reported herein realizes a novel tractable approach for the validation of image-to-physical registration methods and demonstrates promising results for our correction method.

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Section: Introductionmentioning

confidence: 99%

Improving Registration Robustness for Image-Guided Liver Surgery in a Novel Human-to-Phantom Data Framework

Collins

Weis

Heiselman

et al. 2017

IEEE Trans. Med. Imaging

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“…D'Apuzzo and Verius 2 used a three-dimensional (3-D) monitoring system (three cameras) to track points in a brain phantom, with reference points painted on the surface. Sun et al, 3 Paul et al, 4 DeLorenzo et al, 5 Hsieh et al, 6 Vivanti et al, 7 and Kumar et al 8 (two cameras) also tracked points on the brain surface, but in this case automatically extracted.…”

Section: Related Workmentioning

confidence: 99%

Superficial vessel reconstruction with a multiview camera system

et al. 2016

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Abstract. We aim at reconstructing superficial vessels of the brain. Ultimately, they will serve to guide the deformation methods to compensate for the brain shift. A pipeline for three-dimensional (3-D) vessel reconstruction using three mono-complementary metal-oxide semiconductor cameras has been developed. Vessel centerlines are manually selected in the images. Using the properties of the Hessian matrix, the centerline points are assigned direction information. For correspondence matching, a combination of methods was used. The process starts with epipolar and spatial coherence constraints (geometrical constraints), followed by relaxation labeling and an iterative filtering where the 3-D points are compared to surfaces obtained using the thin-plate spline with decreasing relaxation parameter. Finally, the points are shifted to their local centroid position. Evaluation in virtual, phantom, and experimental images, including intraoperative data from patient experiments, shows that, with appropriate camera positions, the error estimates (root-mean square error and mean error) are ∼1 mm.

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“…Landmarks selection repeatability was measured and turned out to be in the same order of magnitude of the navigation system registration error, which is reported to be <1 mm [30]. According to previous studies, the possibility of performing repeated measurements on the same points is a benefit, since it allows monitoring of the brain surface changes over time [31]. First, we point out that the low variability of β is mainly because 10 out of 12 patients considered in this study underwent temporal resection and head placement in this kind of procedure is standard.…”

Section: Discussionmentioning

confidence: 99%

A method for the assessment of time-varying brain shift during navigated epilepsy surgery

et al. 2015

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Purpose Image guidance is widely used in neurosurgery. Tracking systems (neuronavigators) allow registering the preoperative image space to the surgical space. The localization accuracy is influenced by technical and clinical factors, such as brain shift. This paper aims at providing quantitative measure of the time-varying brain shift during open epilepsy surgery, and at measuring the pattern of brain deformation with respect to three potentially meaningful parameters: craniotomy area, craniotomy orientation and gravity vector direction in the images reference frame. Methods We integrated an image-guided surgery system with 3D Slicer, an open-source package freely available in the Internet. We identified the preoperative position of several cortical features in the image space of 12 patients, inspecting both the multiplanar and the 3D reconstructions. We subsequently repeatedly tracked their position in the surgical space. Therefore, we measured the cortical shift, following its time-related changes and estimating its correlation with gravity and craniotomy normal directions. Results The mean of the median brain shift amount is 9.64 mm (SD = 4.34 mm). The brain shift amount resulted not correlated with respect to the gravity direction, the craniotomy normal, the angle between the gravity and the craniotomy normal and the craniotomy area. Conclusions Our method, which relies on cortex surface 3D measurements, gave results, which are consistent with liter-

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A surface registration method for quantification of intraoperative brain deformations in image-guided neurosurgery

Cited by 37 publications

References 32 publications

Improving Registration Robustness for Image-Guided Liver Surgery in a Novel Human-to-Phantom Data Framework

Improving Registration Robustness for Image-Guided Liver Surgery in a Novel Human-to-Phantom Data Framework

Superficial vessel reconstruction with a multiview camera system

A method for the assessment of time-varying brain shift during navigated epilepsy surgery

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