Coregistered Ultrasound as a Neurosurgical Guide

Pallatroni, Henry F.; Hartov, Alex; McInerney, Joseph J.; Platenik, Leah A.; Miga, Michael I.; Kennedy, Francis E.; Paulsen, Keith D.; Roberts, David W.

doi:10.1159/000029775

Cited by 17 publications

(8 citation statements)

References 4 publications

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“…The US transducer was tracked by the Polaris System continuously through a rigidly attached infrared light-emitting tracker to allow for the 3-dimensional US images to be registered to the pMR stack [18,19] .…”

Section: Intraoperative Brain Deformation Proceduresmentioning

confidence: 99%

Estimation of Brain Deformation for Volumetric Image Updating in Protoporphyrin IX Fluorescence-Guided Resection

Valdés

Fan

et al. 2009

Stereotact Funct Neurosurg

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Introduction: Fluorescence-guided resection (FGR) of brain tumors is an intuitive, practical and emerging technology for visually delineating neoplastic tissue exposed intraoperatively. Image guidance is the standard technique for producing 3-dimensional spatially coregistered information for surgical decision making. Both technologies together are synergistic: the former detects surface fluorescence as a biomarker of the current surgical margin while the latter shows coregistered volumetric neuroanatomy but can be degraded by intraoperative brain shift. We present the implementation of deformation modeling for brain shift compensation in protoporphyrin IX FGR, integrating these two sources of information for maximum surgical benefit. Methods: Two patients underwent FGR coregistered with conventional image guidance. Histopathological analysis, intraoperative fluorescence and image space coordinates were recorded for biopsy specimens acquired during surgery. A biomechanical brain deformation model driven by intraoperative ultrasound data was used to generate updated MR images. Results: Combined use of fluorescence signatures and updated MR image information showed substantially improved accuracy compared to fluorescence or the original (i.e., nonupdated) MR images, detecting only true positives and true negatives, and no instances of false positives or false negatives. Conclusion: Implementation of brain deformation modeling in FGR shows promise for increasing the accuracy of neurosurgical guidance in the delineation and resection of brain tumors.

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Section: Intraoperative Brain Deformation Proceduresmentioning

confidence: 99%

Estimation of Brain Deformation for Volumetric Image Updating in Protoporphyrin IX Fluorescence-Guided Resection

Valdés

Fan

et al. 2009

Stereotact Funct Neurosurg

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“…Image transformation is illustrated in Fig. 2a, in which tracker T US was obtained by scan-head calibration (accuracy of approximately 2 mm; [4]). An arbitrary 3DUS image was transformed into a pre-selected 3DUS volume (chosen as the first 3DUS image acquired for each patient) coordinate system by:

{{}^{U S 1}T}_{U S 2} = inv ({{}^{tracker}T}_{U S}) \times inv ({{}^{patient}T}_{tracker}^{(1)}) \times {{}^{patient}T}_{tracker}^{(2)} \times {{}^{tracker}T}_{U S},

…”

Section: Methodsmentioning

confidence: 99%

“…The accuracy of the image transformation depended on the accuracies of the Polaris tracker (error <1 mm) as well as the US scan-head calibration (error of 2–3 mm; [4]). To improve the accuracy of image transformation, an inter-image re-registration was performed to maximize image alignment (see 2.3 for details) before rasterization.…”

Section: Methodsmentioning

confidence: 99%

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009

Yang

Hawkes

Rueckert

et al. 2009

Lecture Notes in Computer Science

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We present an accurate and efficient technique to combine and rasterize multiple 3D ultrasound (3DUS) image volumes originally presented in spherical coordinates into a single, 3D Cartesian image that uniformly samples the total field of view. To ensure the consistency of merged image content in overlapping regions, image re-registration was performed by maximizing mutual information (MI). The technique was applied to 22 3DUS image volumes obtained during five neurosurgical patient cases. The computational cost of the approach increases linearly with the number of images involved (average time to combine and rasterize one pair of 3DUS images was 1.5 sec). Interpolation was approximately 20% more accurate in overlapping regions when reregistration was performed before rasterization and minimized feature loss and/or blurring that was evident without re-registration. In addition, we report the average translational (35.2 mm) and rotational (38.5°) capture ranges for the MI re-registration of two volumetric 3DUS images. The technique is applicable in any clinical application in which volumetric true 3DUS is acquired.

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“…Intraoperative ultrasound, initially tracked using an electromagnetic digitizer [11,12], has been adapted to an optical digitizer, and a new calibration algorithm implemented. Accuracy has been assessed in a phantom and in a pig model.…”

Section: Methodsmentioning

confidence: 99%

Intra-Operative Image Updating

Roberts

Lunn²,

Sun³

et al. 2001

Stereotact Funct Neurosurg

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Intraoperative brain shift and deformation pose challenges for image-guided surgery. One strategy to address these problems utilizes computational modeling coupled with intraoperatively acquired information from efficient and economical sources such as ultrasound and the optics of the operating microscope. Calibration algorithms for the accurate integration of these sparse data sources have been implemented. Assessment has been performed in both phantom and pig brain models, and accuracy better than 2 mm has been achieved. Methods of incorporating these data into the computational model are being developed.

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Coregistered Ultrasound as a Neurosurgical Guide

Cited by 17 publications

References 4 publications

Estimation of Brain Deformation for Volumetric Image Updating in Protoporphyrin IX Fluorescence-Guided Resection

Estimation of Brain Deformation for Volumetric Image Updating in Protoporphyrin IX Fluorescence-Guided Resection

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009

Intra-Operative Image Updating

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