Intraoperative Imaging with a Therapeutic Computed Tomographic Scanner

Lunsford, L. Dade; Parrish, Rob G.; Albright, Leland

doi:10.1227/00006123-198410000-00017

Cited by 98 publications

(24 citation statements)

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“…where X 1 is the matrix containing the displacement field vectors for all points in the brain at the various orientations/CSF drainage levels and x is a vector of coefficients obtained from solving equation (4).…”

Section: Fig 3 Statistical Modelmentioning

confidence: 99%

“…To correct for deformations, various imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound (US) have been used for intraoperative image-guided surgery, and each imaging procedure has its inherent advantages and disadvantages [3][4][5]. While CT and MR procedures have been labeled cumbersome and have been questioned for their cost-effectiveness, US lacks the image clarity that CT and MR scans produce.…”

Section: Introductionmentioning

confidence: 99%

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Model-Updated Image Guidance: A Statistical Approach to Gravity-Induced Brain Shift

Dumpuri¹,

Chen²,

Miga³

2003

Lecture Notes in Computer Science

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Abstract. Compensating for intraoperative brain shift using computational models has been used with promising results. Since computational time is an important factor during neurosurgery, a prior knowledge of a patient's orientation and changes in tissue buoyancy force would be valuable information to aid in predicting shift due to gravitational forces. Since the latter is difficult to quantify intraoperatively, a statistical model for predicting intraoperative brain deformations due to gravity is reported. This statistical model builds on a computational model developed earlier. For a given set of patient's orientation and amount of CSF drainage, the intraoperative brain shift is calculated using the computational model. These displacements are then validated against measured displacements to predict the intraoperative brain shift. Though initial results are promising, further study is needed before the statistical model can be used for model-updated image-guided surgery.

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Section: Fig 3 Statistical Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model-Updated Image Guidance: A Statistical Approach to Gravity-Induced Brain Shift

Dumpuri¹,

Chen²,

Miga³

2003

Lecture Notes in Computer Science

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“…Interventional imaging is typically performed with X-ray fluoroscopy or ultrasound [2]. More recently, technological developments have led to high-resolution, highcontrast 3D interventional imaging, such as interventional CT [8], and MRI [12]. Compared to the other named interventional imaging modalities, an open MR system provides the advantages of high soft-tissue contrast, a lack of radiation exposure to patient and surgeon, a clear definition of resection boundaries, and continuous access to the operative field [7].…”

Section: Limitations Of Conventional Image-guided Surgerymentioning

confidence: 99%

An Integrated Visualization System for Surgical Planning and Guidance Using Image Fusion and Interventional Imaging

Gering¹,

Nabavi

Kikinis

et al. 1999

Lecture Notes in Computer Science

150

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Abstract. We present a software package which uniquely integrates several facets of image-guided medicine into a single portable, extendable environment. It provides capabilities for automatic registration, semiautomatic segmentation, 3D surface model generation, 3D visualization, and quantitative analysis of various medical scans. We describe its system architecture, wide range of applications, and novel integration with an interventional Magnetic Resonance (MR) scanner to augment intraoperative imaging with pre-operative data. Analysis previously reserved for pre-operative data can now be applied to exploring the anatomical changes as the surgery progresses. Surgical instruments are tracked and used to drive the location of reformatted slices. Real-time scans are visualized as slices in the same 3D view along with the pre-operative slices and surface models. The system has been applied in over 20 neurosurgical cases at Brigham and Women's Hospital, and continues to be routinely used for 1-3 cases per week.

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“…Intraoperative imaging would include the use of computed tomography (iCT), magnetic resonance (iMR), and/or ultrasound (iUS) imaging. In the 1980s, there was a significant effort to introduce iCT, but concerns regarding patient radiation, the need for radiological staffing of the operating room (OR), and the cumbersome lead protection seemed to adversely affect the adoption of this technique [17]. Several medical centers are now deploying iMR imaging capabilities [18], [19] and have developed elegant and sophisticated methods for visualization in the OR [4], [20], [21].…”

Section: Introductionmentioning

confidence: 99%

Cortical surface registration for image-guided neurosurgery using laser-range scanning

Miga

Sinha

Cash

et al. 2003

IEEE Trans. Med. Imaging

140

118

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In this paper, a method of acquiring intraoperative data using a laser range scanner (LRS) is presented within the context of model-updated image-guided surgery. Registering textured point clouds generated by the LRS to tomographic data is explored using established point-based and surface techniques as well as a novel method that incorporates geometry and intensity information via mutual information (SurfaceMI). Phantom registration studies were performed to examine accuracy and robustness for each framework. In addition, an in vivo registration is performed to demonstrate feasibility of the data acquisition system in the operating room. Results indicate that SurfaceMI performed better in many cases than point-based (PBR) and iterative closest point (ICP) methods for registration of textured point clouds. Mean target registration error (TRE) for simulated deep tissue targets in a phantom were 1.0 ± 0.2, 2.0 ± 0.3, and 1.2 ± 0.3 mm for PBR, ICP, and SurfaceMI, respectively. With regard to in vivo registration, the mean TRE of vessel contour points for each framework was 1.9 ± 1.0, 0 9 ± 0.6, and 1.3 ± 0.5 for PBR, ICP, and SurfaceMI, respectively. The methods discussed in this paper in conjunction with the quantitative data provide impetus for using LRS technology within the model-updated image-guided surgery framework.

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Intraoperative Imaging with a Therapeutic Computed Tomographic Scanner

Cited by 98 publications

References 0 publications

Model-Updated Image Guidance: A Statistical Approach to Gravity-Induced Brain Shift

Model-Updated Image Guidance: A Statistical Approach to Gravity-Induced Brain Shift

An Integrated Visualization System for Surgical Planning and Guidance Using Image Fusion and Interventional Imaging

Cortical surface registration for image-guided neurosurgery using laser-range scanning

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