Determination of biplane geometry and centerline curvature in vascular imaging

Nazareth, D; Hoffmann, Kenneth R.; Walczak, Alan M.; Dmochowski, Jacek; Guterman, Lee R.; Rudin, Stephen; Bednarek, Daniel R.

doi:10.1117/12.467237

Cited by 4 publications

(5 citation statements)

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“…We also made significant progress in determining vessel parameters like tortuosity and curvature metrics 1,2,3 . This information was determined without considering the fitness of the stent.…”

Section: Methodsmentioning

confidence: 99%

“…In this paper, we describe our efforts at developing a novel technique to compute the stent-fitness measures based on patient-specific vasculature data (3D point cloud, centerline, and the radius data ( Fig.1 left) of the vasculature) which is obtained from multiple views of 2D angiograms 1,2 . This method uses the geometric structure of the reconstructed 3D vasculature applying configuration-space techniques already developed in our labs 3 .…”

Section: Introductionmentioning

confidence: 99%

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Configuration-space technique for calculating stent-fitness measures for the planning of neuro-endovascular interventions

2005

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This paper demonstrates a new technique to compute stent-fitness measures for a vascular anatomy, using geometric information. This technique will aid the interventionalist in treatment planning for Neuro-endovascular interventions. Patient-specific vessel-surface reconstruction is performed from point/contour data without user intervention. The technique developed is based on configuration-space algorithms, which are widely used in robot motion planning. A fitness measure is computed for stents with various parameters for a patient-specific vessel data. Finally, a simulation is performed to check for collisions. This feature will provide an additional tool to the interventionalist for the planning of neuro-endovascular interventions, with the dimensions of the stent based on proximal and distal neck of the aneurysm for a patient-specific vascular anatomy.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Configuration-space technique for calculating stent-fitness measures for the planning of neuro-endovascular interventions

2005

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“…We have developed a method for determination of the 3D carotid centerline [1,2] and aneurysm geometry from 2D biplane X-ray angiograms. The user need only indicate the vessel centerline in both images and the boundary of the aneurysm.…”

Section: Data Acquisitionmentioning

confidence: 99%

Geometry-based metrics for planning of neuroendovascular therapy

Subramanian

Kesavadas

Hoffmann

2004

International Congress Series

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“…Several interesting techniques have been proposed, 15 each adopting a novel strategy to obtain an estimate of the imaging geometry-either by optimizing the agreement of the reconstructed 3D with the 2D set of points 5,8,10 or adopting a nonlinear optimization strategy 7 or by generalizing the epipolar constraints by applying it to a set of curves, such as a network of vessels. 9 In a recent article, 16 Xu et al, proposed transforming the geometry determination problem into a geometric-search problem in the six-dimensional ͑6D͒ R − t space. In their technique, they identify R − t combinations that could be consistent with selected levels of uncertainty in the image data repeatedly decreasing the uncertainty until a single R − t solution is determined.…”

Section: Introductionmentioning

confidence: 99%

“…To circumvent this problem, several researchers proposed using bifurcation points in vessels which are visible in both views to determine the imaging geometry. [4][5][6][7][8][9][10] More recently, some investigators adopted an approach 9 which required identification of corresponding vessel segments in both views; an objective function was then optimized based on the epipolar constraints. Chen and Metz 7 proposed formulating the problem as a constrained nonlinear optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Towards a theory of a solution space for the biplane imaging geometry problem

Singh

Hoffmann

et al. 2006

Medical Physics

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Biplane angiographic imaging is a primary method for visual and quantitative assessment of the vasculature. In order to reliably reconstruct the three-dimensional (3D) position, orientation, and shape of the vessel structure, a key problem is to determine the rotation matrix R and the translation vector t which relate the two coordinate systems. This so-called Imaging Geometry Determination problem is well studied in the medical imaging and computer vision communities and a number of interesting approaches have been reported. Each such technique determines a solution which yields 3D vasculature reconstructions with errors comparable to other techniques. From the literature, we see that different techniques with different optimization strategies yield reconstructions with equivalent errors. We have investigated this behavior, and it appears that the error in the input data leads to this equivalence effectively yielding what we call the solution space of feasible geometries, i.e., geometries which could be solutions given the error or uncertainty in the input image data. In this paper, we lay the theoretical framework for this concept of a solution space of feasible geometries using simple schematic constructions, deriving the underlying mathematical relationships, presenting implementation details, and discussing implications and applications of the proposed idea. Because the solution space of feasible geometries encompasses equivalent solutions given the input error, the solution space approach can be used to evaluate the precision of calculated geometries or 3D data based on known or estimated uncertainties in the input image data. We also use the solution space approach to calculate an imaging geometry, i.e., a solution.

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Determination of biplane geometry and centerline curvature in vascular imaging

Cited by 4 publications

References 0 publications

Configuration-space technique for calculating stent-fitness measures for the planning of neuro-endovascular interventions

Configuration-space technique for calculating stent-fitness measures for the planning of neuro-endovascular interventions

Geometry-based metrics for planning of neuroendovascular therapy

Towards a theory of a solution space for the biplane imaging geometry problem

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