3D reconstruction of the carotid artery from two views using a single centerline

Hoffmann, Kenneth R.; Walczak, Alan M.; Noël, Peter B.

doi:10.1016/j.ics.2004.03.145

Cited by 6 publications

(4 citation statements)

References 19 publications

(5 reference statements)

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“…These two-dimensional image sequences of the vasculature are transferred to the analysis computer. Using an in-house-developed interface, vessels in the images are selected, and the centerlines are indicated and fit using cubic splines [7].…”

Section: Methodsmentioning

confidence: 99%

Generating of 3D data during neurovascular interventions by using multi-projection imaging

Noël

Hoffmann

Walczak

et al. 2005

International Congress Series

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Section: Methodsmentioning

confidence: 99%

Generating of 3D data during neurovascular interventions by using multi-projection imaging

Noël

Hoffmann

Walczak

et al. 2005

International Congress Series

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“…Here we determine the centerlines based on user indicated points and fitting these points using cubic splines. 20 They can be determined using vessel tracking techniques as well, 16,17 and the determined centerlines can then be refined using other techniques. 18,19 Note that corresponding centerlines not corresponding points are used in this technique.…”

Section: Optimization Of Multiple Angiogramsmentioning

confidence: 99%

Optimization of three‐dimensional angiographic data obtained by self‐calibration of multiview imaging

et al. 2006

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Stroke is one of the leading causes of death in the U.S. The treatment of stroke often involves vascular interventions in which devices are guided to the intervention site often through tortuous vessels based on two-dimensional (2-D) angiographic images. Three dimensional (3-D) vascular information may facilitate these procedures. Methods have been proposed for the self-calibrating determination of 3-D vessel trees from biplane and multiple plane images and the geometric relationships between the views (imaging geometries). For the biplane analysis, four or more corresponding points must be identified in the biplane images. For the multiple view technique, multiple vessels must be indicated and only the translation vectors relating the geometries are calculated. We have developed methods for the calculation of the 3-D vessel data and the full transformations relating the multiple views (rotations and translations) obtained during interventional procedures, and the technique does not require indication of corresponding points, but only the indication of a single vessel, e.g., the vessel of interest. Multiple projection views of vessel trees are obtained and transferred to the analysis computer. The vessel or vessels of interest are indicated by the user. Using the initial imaging geometry determined from the gantry information, 3-D vessel centerlines are calculated using the indicated centerlines in pairs of images. The imaging geometries are then iteratively adjusted and 3-D centerlines recalculated until the root-mean-square (rms) difference between the calculated 3-D centerlines is minimized. Simulations indicate that the 3-D centerlines can be accurately determined (to within 1 mm) even for errors in indication of the vessel endpoints as large as 5 mm. In phantom studies, the average rms difference between the pairwise calculated 3-D centerlines is approximately 7.5 mm prior to refinement (i.e., using the gantry information alone), whereas the average rms difference is usually below 1 mm after refinement. Accuracies and reliabilities of better than 1 mm were also determined by comparing centerlines determined using multiview and rotational angiography reconstruction and clinical data sets. These results indicate that the multiview approach will provide accurate and reliable 3-D centerlines for indicated vessel(s) without increasing the dose to the patient.

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“…The left and right coronary systems in the heart (7,8,10,13,(16)(17)(18)(19) and the internal carotids (27) and/or the cranial vessels distal to the Circle of Willis (28) are reconstructed using two views. Thus, we chose to generate from four distributions to represent the configurations reconstructed clinically.…”

Section: Iib Shapementioning

confidence: 99%

“…Among the shapes that are encountered clinically are those of the heart and the neurovasculature. The left and right coronary systems in the heart 7,8,10,13,[16][17][18][19] and the internal carotids 27 and/or the cranial vessels distal to the Circle of Willis 28 are reconstructed using two views ͑Fig. 2͒.…”

Section: Iib Shapementioning

confidence: 99%

Effects of point configuration on the accuracy in 3D reconstruction from biplane images

et al. 2005

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Two or more angiograms are being used frequently in medical imaging to reconstruct locations in three-dimensional (3D) space, e.g., for reconstruction of 3D vascular trees, implanted electrodes, or patient positioning. A number of techniques have been proposed for this task. In this simulation study, we investigate the effect of the shape of the configuration of the points in 3D (the "cloud" of points) on reconstruction errors for one of these techniques developed in our laboratory. Five types of configurations (a ball, an elongated ellipsoid (cigar), flattened ball (pancake), flattened cigar, and a flattened ball with a single distant point) are used in the evaluations. For each shape, 100 random configurations were generated, with point coordinates chosen from Gaussian distributions having a covariance matrix corresponding to the desired shape. The 3D data were projected into the image planes using a known imaging geometry. Gaussian distributed errors were introduced in the x and y coordinates of these projected points. Gaussian distributed errors were also introduced into the gantry information used to calculate the initial imaging geometry. The imaging geometries and 3D positions were iteratively refined using the enhanced-Metz-Fencil (EMF) technique. The image data were also used to evaluate the feasible R-t solution volume. The 3D errors between the calculated and true positions were determined. The effects of the shape of the configuration, the number of points, the initial geometry error, and the input image error were evaluated. The results for the number of points, initial geometry error, and image error are in agreement with previously reported results, i.e., increasing the number of points and reducing initial geometry and/or image error, improves the accuracy of the reconstructed data. The shape of the 3D configuration of points also affects the error of reconstructed 3D configuration; specifically, errors decrease as the "volume" of the 3D configuration increases, as would be intuitively expected, and shapes with larger spread, such as spherical shapes, yield more accurate reconstructions. These results are in agreement with an analysis of the solution volume of feasible geometries and could be used to guide selection of points for reconstruction of 3D configurations from two views.

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3D reconstruction of the carotid artery from two views using a single centerline

Cited by 6 publications

References 19 publications

Generating of 3D data during neurovascular interventions by using multi-projection imaging

Generating of 3D data during neurovascular interventions by using multi-projection imaging

Optimization of three‐dimensional angiographic data obtained by self‐calibration of multiview imaging

Effects of point configuration on the accuracy in 3D reconstruction from biplane images

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