Vessel Driven Correction of Brain Shift

Reinertsen, Ingerid; Descoteaux, Maxime; Drouin, Simon; Siddiqi, Kaleem; Collins, D. Louis

doi:10.1007/978-3-540-30136-3_27

Cited by 21 publications

(27 citation statements)

References 14 publications

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“…In the series of cases reported here, two automatic techniques for MRI-ultrasound rigid body registration were tested intraoperatively: a vessel-based technique [57,58] and a method based on mutual information [59]. For each case, the best visual result was chosen for display to the surgeon.…”

Section: Intraoperative Mri-ultrasound Realignmentmentioning

confidence: 99%

New prototype neuronavigation system based on preoperative imaging and intraoperative freehand ultrasound: system description and validation

et al. 2010

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Section: Intraoperative Mri-ultrasound Realignmentmentioning

confidence: 99%

New prototype neuronavigation system based on preoperative imaging and intraoperative freehand ultrasound: system description and validation

et al. 2010

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“…The work is based on experiments first presented in Reinertsen et al (2004) where we demonstrated that it was possible to use vesselbased non-linear registration for this task. In this paper, we have further developed and improved our vessel based registration method and present experimental validation of the technique.…”

Section: Vessel Based Registrationmentioning

confidence: 99%

Validation of vessel-based registration for correction of brain shift

Reinertsen

Descoteaux

Siddiqi

et al. 2007

Medical Image Analysis

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The displacement and deformation of brain tissue is a major source of error in image-guided neurosurgery systems. We have designed and implemented a method to detect and correct brain shift using pre-operative MR images and intraoperative Doppler ultrasound data and present its validation with both real and simulated data. The algorithm uses segmented vessels from both modalities, and estimates the deformation using a modified version of the iterative closest point (ICP) algorithm. We use the least trimmed squares (LTS) to reduce the number of outliers in the point matching procedure. These points are used to drive a thin-plate spline transform to achieve non-linear registration. Validation was completed in two parts. First, the technique was tested and validated using realistic simulations where the results were compared to the known deformation. The registration technique recovered 75% of the deformation in the region of interest accounting for deformations as large as 20 mm. Second, we performed a PVA-cryogel phantom study where both MR and ultrasound images of the phantom were obtained for three different deformations. The registration results based on MR data were used as a gold standard to evaluate the performance of the ultrasound based registration. On average, deformations of 7.5 mm magnitude were corrected to within 1.6 mm for the ultrasound based registration and 1.07 mm for the MR based registration.

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“…The procedure reduces the amount of time required for manual preparation and structure segmentation. In addition, the vessel segmentation procedure is being used in a study of vessel driven brain shift correction at the MNI, where pre-operative MR images and intraoperative Doppler ultrasound data serve as features in a non-linear registration paradigm to correct for brain-shift during surgery [41].…”

Section: Conclusion and Discussionmentioning

confidence: 99%

A geometric flow for segmenting vasculature in proton-density weighted MRI

Descoteaux

Collins

Siddiqi

2008

Medical Image Analysis

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Modern neurosurgery takes advantage of magnetic resonance images (MRI) of a patient's cerebral anatomy and vasculature for planning before surgery and guidance during the procedure. Dual echo acquisitions are often performed that yield proton density (PD) and T2-weighted images to evaluate edema near a tumor or lesion. In this paper we develop a novel geometric flow for segmenting vasculature in PD images, which can also be applied to the easier cases of MR angiography data or Gadolinium enhanced MRI. Obtaining vasculature from PD data is of clinical interest since the acquisition of such images is widespread, the scanning process is non-invasive, and the availability of vessel segmentation methods could obviate the need for an additional angiographic or contrast-based sequence during preoperative imaging. The key idea is to first apply Frangi's vesselness measure [1] to find putative centerlines of tubular structures along with their estimated radii. This measure is then distributed to create a vector field which allows the flux maximizing flow algorithm of [2] to be applied to recover vessel boundaries. We carry out a qualitative validation of the approach on PD, MR angiography and Gadolinium enhanced MRI volumes and suggest a new way to visualize the segmentations in 2D with masked projections. We validate the approach quantitatively on a single-subject data set consisting of PD, phase contrast (PC) angiography and time of flight (TOF) angiography volumes, with an expert segmented version of the TOF volume viewed as the ground truth. We then validate the approach quantitatively on 19 PD data sets from a new digital brain phantom, with semi-automatically obtained labels from the corresponding angiography volumes viewed as ground truth. A significant finding is that both for the single-subject and multi-subject studies, 90% or more of the vasculature in the ground truth segmentation is recovered from the automatic segmentation of the other volumes.

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Vessel Driven Correction of Brain Shift

Cited by 21 publications

References 14 publications

New prototype neuronavigation system based on preoperative imaging and intraoperative freehand ultrasound: system description and validation

New prototype neuronavigation system based on preoperative imaging and intraoperative freehand ultrasound: system description and validation

Validation of vessel-based registration for correction of brain shift

A geometric flow for segmenting vasculature in proton-density weighted MRI

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