Automated brain shift correction using a pre-computed deformation atlas

Dumpuri, Prashanth; Thompson, Reid C.; Sinha, Tuhin; Miga, Michael I.

doi:10.1117/12.652350

Cited by 10 publications

(11 citation statements)

References 14 publications

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“…Full volumetric compensation is still necessary for deep-seated lesions and strategies to achieve this are in progress. 18,23 As a means to qualitatively demonstrate intraoperative brain surface deformation, the high-resolution texture images of the cases with maximum swelling ͑patient 8͒ and sagging ͑patient 7͒ are illustrated in Fig. 8.…”

Section: Discussionmentioning

confidence: 99%

Laser range scanning for image‐guided neurosurgery: Investigation of image‐to‐physical space registrations

et al. 2008

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In this article a comprehensive set of registration methods is utilized to provide image-to-physical space registration for image-guided neurosurgery in a clinical study. Central to all methods is the use of textured point clouds as provided by laser range scanning technology. The objective is to perform a systematic comparison of registration methods that include both extracranial ͑skin marker point-based registration ͑PBR͒, and face-based surface registration͒ and intracranial methods ͑fea-ture PBR, cortical vessel-contour registration, a combined geometry/intensity surface registration method, and a constrained form of that method to improve robustness͒. The platform facilitates the selection of discrete soft-tissue landmarks that appear on the patient's intraoperative cortical surface and the preoperative gadolinium-enhanced magnetic resonance ͑MR͒ image volume, i.e., true corresponding novel targets. In an 11 patient study, data were taken to allow statistical comparison among registration methods within the context of registration error. The results indicate that intraoperative face-based surface registration is statistically equivalent to traditional skin marker registration. The four intracranial registration methods were investigated and the results demonstrated a target registration error of 1.6Ϯ 0.5 mm, 1.7Ϯ 0.5 mm, 3.9Ϯ 3.4 mm, and 2.0Ϯ 0.9 mm, for feature PBR, cortical vessel-contour registration, unconstrained geometric/intensity registration, and constrained geometric/intensity registration, respectively. When analyzing the results on a per case basis, the constrained geometric/intensity registration performed best, followed by feature PBR, and finally cortical vessel-contour registration. Interestingly, the best target registration errors are similar to targeting errors reported using bone-implanted markers within the context of rigid targets. The experience in this study as with others is that brain shift can compromise extracranial registration methods from the earliest stages. Based on the results reported here, organ-based approaches to registration would improve this, especially for shallow lesions.

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Section: Discussionmentioning

confidence: 99%

Laser range scanning for image‐guided neurosurgery: Investigation of image‐to‐physical space registrations

et al. 2008

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“…Stereoscopic cameras and LRSs have been highly investigated sources of sparse data in the recent literature and have been used extensively to capture brain surface deformations [56][57][58][59], liver [26,61,62] and kidney organ surfaces [21,62], as well as breast [63]. Some comparisons of the computer vision approaches have been made too [64]. With respect to modeling approaches to compensate for brain deformations, the growth in this literature has been considerable.…”

Section: Organ Deformationmentioning

confidence: 99%

Organ Deformation and Navigation

Gallowayjr

Miga

2015

Imaging and Visualization in the Modern Operating Room

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As image-guided surgery (IGS) expands beyond intracranial neurosurgery, it takes on additional challenges. Organs other than the brain lack the encompassing bone of the skull which allows them to move and deform as a result of operative pose, respiratory and cardiac motion, and tractions placed on the tissue during the interventional process. Additionally, the skull was easily accessible via a minor incision into the skin. That allowed for the implantation of rigid reference points, or fiducial markers, which allowed for easy registration of tomographic spaces and physical space [1][2][3]. In other organs that rigid platform was not available. So both additional ways of using image information to guide procedures and techniques to account for deformation had to be developed.Image-guided procedures require a methodology of matching imaging data with physiological position and tool orientation. This requires either a registration or a calibration. The difference is in the application. In a calibration, a device is created which can reach any point within a tomographic scanner (computed tomography, CT; magnetic resonance imaging, MRI; positron emission tomography, PET; or single-photon emission computed tomography, SPECT). A phantom object containing easy-to-locate and well-described points is first volumetrically scanned and then located by the device. This allows the determination of the mathematical relationship between localizer position and orientation to locations within the scanner. Then, when a patient is scanned, the image information is used to find targets of interest in the images and thus scanner space [4]. The localization device is then used to reach the point in scanner space. If desired, a second scan can be performed to confirm the accuracy.Using ultrasound as a guidance method can also be considered a calibration. Here, a tracking system is attached to an ultrasound system and the mathematical relationship between the tracked ultrasound and its image slice is determined by a phantom process [5,6]. That allows the location of objects seen in the ultrasound to be determined in tracking system space. Then a tracked surgical instrument (also localized in tracking system space) can move to the location determined in the ultrasound (see Fig. 9.1).The strength of a calibration technique is that it requires no processing of the images and the time between obtaining the scan and the interventional procedure is minimized [7]. However, the most important issue to be addressed in a tomographic calibration technique is that no motion or deformation has occurred between the acquisition and Y. Fong et al. (eds.), Imaging and Visualization in The Modern Operating Room,

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“…The problem is solved by deforming the preoperative brain to intraoperative data based on BSM, which predicts the brain deformation. Dumpuri et al [73] predicted intraoperative brain deformation by combining a statistical model of brain shift and sparse 3D points on the intraoperative brain surface measured by laser range scanner. Using the brain deformation model [74], their statistical model is constructed by a range of possible brain deformation simulation by changing boundary conditions.…”

Section: Neurosurgerymentioning

confidence: 99%

A Survey on Statistical Modeling and Machine Learning Approaches to Computer Assisted Medical Intervention: Intraoperative Anatomy Modeling and Optimization of Interventional Procedures

Morooka

Nakamoto

Sato

2013

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper reviews methods for computer assisted medical intervention using statistical models and machine learning technologies, which would be particularly useful for representing prior information of anatomical shape, motion, and deformation to extrapolate intraoperative sparse data as well as surgeons' expertise and pathology to optimize interventions. Firstly, we present a review of methods for recovery of static anatomical structures by only using intraoperative data without any preoperative patient-specific information. Then, methods for recovery of intraoperative motion and deformation are reviewed by combining intraoperative sparse data with preoperative patient-specific stationary data, which is followed by a survey of articles which incorporated biomechanics. Furthermore, the articles are reviewed which addressed the used of statistical models for optimization of interventions. Finally, we conclude the survey by describing the future perspective.

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Automated brain shift correction using a pre-computed deformation atlas

Cited by 10 publications

References 14 publications

Laser range scanning for image‐guided neurosurgery: Investigation of image‐to‐physical space registrations

Laser range scanning for image‐guided neurosurgery: Investigation of image‐to‐physical space registrations

Organ Deformation and Navigation

A Survey on Statistical Modeling and Machine Learning Approaches to Computer Assisted Medical Intervention: Intraoperative Anatomy Modeling and Optimization of Interventional Procedures

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