Image Updating for Brain Shift Compensation During Resection

Fan, Xiaoyao; Roberts, David W.; Olson, Jonathan D.; Ji, Songbai; Schaewe, Timothy J.; Simon, David A.; Paulsen, Keith D.

doi:10.1093/ons/opx123

Cited by 23 publications

(25 citation statements)

References 27 publications

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“…They are significantly harder to read, and especially determining the boundaries of the regions of interest is difficult. Because preoperative MR images are more detailed, several works [1][2][3][4][5][6][7] proposed methods to register intraoperative ultrasound and preoperative MR images. Some methods use a biomechanical model of the brain in addition to image processing techniques.…”

Section: Contextmentioning

confidence: 99%

“…Model-based methods [1][2][3][4][5] use a biomechanical model of the brain to estimate the deformation. In order to take into account the tissue resection, the resection cavity needs to be segmented so that the corresponding nodes can be removed from the model.…”

Section: Contextmentioning

confidence: 99%

“…However, the shape of the resection cavity is often more complex. In Fan et al, 5 a first estimation of the cavity is determined with intraoperative stereovision images using the difference between the 3D surfaces before and after resection. The model is run with this estimation of the cavity.…”

Section: Contextmentioning

confidence: 99%

See 2 more Smart Citations

Automatic segmentation of brain tumor resections in intraoperative ultrasound images

Carton

Noble

Chabanas

2019

Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling

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The brain is significantly deformed during neurosurgery, in particular because of the removal of tumor tissue. Because of this deformation, intraoperative data is needed for accurate navigation in image-guided surgery. During the surgery, it is easier to acquire ultrasound images than Magnetic Resonance (MR) images. However, ultrasound images are difficult to interpret. Several methods have been developed to register preoperative MR and intraoperative ultrasound images, to allow accurate navigation during neurosurgery. Model-based methods need the location of the resection cavity to take into account the tissue removal in the model. Manually segmenting this cavity is extremely time consuming and cannot be performed in the operating room. It is also difficult and error-prone because of the noise and reconstruction artifacts in the ultrasound images. In this work, we present a method to perform the segmentation of the resection cavity automatically. We manually labelled the resection cavity on the ultrasound volumes from a database of 23 patients. We trained a Unet-based artificial neural network with our manual segmentation and evaluated several variations of the method. Our best method results in 0.86 mean Dice score over the 10 testing cases. The Dice scores range from 0.68 to 0.96, and nine out of ten are higher than 0.75. For the most difficult test cases, lacking clear contour, the manual segmentation is also difficult but our method still yields acceptable results. Overall the segmentations obtained with the automatic methods are qualitatively similar to the manual ones.

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Section: Contextmentioning

confidence: 99%

Section: Contextmentioning

confidence: 99%

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Automatic segmentation of brain tumor resections in intraoperative ultrasound images

Carton

Noble

Chabanas

2019

Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling

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“…Images can be mapped using fiducials 8 or imagebased registration methods. [9][10][11] Other authors use a biomechanical model for the registration, 12 sometimes with the removal of elements (or anniliation of their mechanical effects) localized inside the resection cavity. 13,14 Nonetheless, and regardless of their accuracy, these methods cannot be widely spread in clinical practice due to the cumbersomeness and cost of intraoperative MR devices.…”

Section: Introductionmentioning

confidence: 99%

“…Their coupling with a mechanical model accounting for the retraction and/or resection yielded to significant results. 11,15 A recent retrospective study also compared outcomes from simulated surface data with actual iMR imaging. 16 Finally, another intraoperative framework is Ultrasound (iUS) imaging.…”

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

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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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Model‐based image updating in deep brain stimulation with assimilation of deep brain sparse data

Fan

Aronson

et al. 2023

Medical Physics

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BackgroundAccuracy of electrode placement for deep brain stimulation (DBS) is critical to achieving desired surgical outcomes and impacts the efficacy of treating neurodegenerative diseases. Intraoperative brain shift degrades the accuracy of surgical navigation based on preoperative images.PurposeWe extended a model‐based image updating scheme to address intraoperative brain shift in DBS surgery and improved its accuracy in deep brain.MethodsWe evaluated 10 patients, retrospectively, who underwent bilateral DBS surgery and classified them into groups of large and small deformation based on a 2 mm subsurface movement threshold and brain shift index of 5%. In each case, sparse brain deformation data were used to estimate whole brain displacements and deform preoperative CT (preCT) to generate updated CT (uCT). Accuracy of uCT was assessed using target registration errors (TREs) at the Anterior Commissure (AC), Posterior Commissure (PC), and four calcification points in the sub‐ventricular area by comparing their locations in uCT with their ground truth counterparts in postoperative CT (postCT).ResultsIn the large deformation group, TREs were reduced from 2.5 mm in preCT to 1.2 mm in uCT (53% compensation); in the small deformation group, errors were reduced from 1.25 to 0.74 mm (41%). Average reduction of TREs at AC, PC and pineal gland were significant, statistically (p ⩽ 0.01).ConclusionsWith more rigorous validation of model results, this study confirms the feasibility of improving the accuracy of model‐based image updating in compensating for intraoperative brain shift during DBS procedures by assimilating deep brain sparse data.

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Image Updating for Brain Shift Compensation During Resection

Abstract: We have developed an image-updating system to compensate for brain deformation during resection using a computational model with data assimilation of displacements measured with intraoperative stereovision imaging that maintains TREs less than 2 mm on average.

Cited by 23 publications

References 27 publications

Automatic segmentation of brain tumor resections in intraoperative ultrasound images

Automatic segmentation of brain tumor resections in intraoperative ultrasound images

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

Model‐based image updating in deep brain stimulation with assimilation of deep brain sparse data

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