Geometrical calibration for 3D x-ray imaging

Rougée, A.; Picard, Catherine; Trousset, Yves; Ponchut, C.

doi:10.1117/12.146963

Cited by 60 publications

(61 citation statements)

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“…These matrices can be obtained either by calculation assuming an ideal geometric model of the C-arm, or by calibration, taking into account the non-ideal behavior of the C-arm system's projection geometry. The calibration of camera systems has been thoroughly studied, and well established techniques exist 8,9 .…”

Section: Methodsmentioning

confidence: 99%

Accuracy of x-ray image-based 3D localization from two C-arm views: a comparison between an ideal system and a real device

et al. 2009

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C-arm X-ray imaging devices are commonly used for minimally invasive cardiovascular or other interventional procedures. Calibrated state-of-the-art systems can, however, not only be used for 2D imaging but also for three-dimensional reconstruction either using tomographic techniques or even stereotactic approaches.To evaluate the accuracy of X-ray object localization from two views, a simulation study assuming an ideal imaging geometry was carried out first. This was backed up with a phantom experiment involving a real C-arm angiography system. Both studies were based on a phantom comprising five point objects. These point objects were projected onto a flat-panel detector under different C-arm view positions. The resulting 2D positions were perturbed by adding Gaussian noise to simulate 2D point localization errors. In the next step, 3D point positions were triangulated from two views. A 3D error was computed by taking differences between the reconstructed 3D positions using the perturbed 2D positions and the initial 3D positions of the five points. This experiment was repeated for various C-arm angulations involving angular differences ranging from 15 o to 165 o . The smallest 3D reconstruction error was achieved, as expected, by views that were 90 o degrees apart. In this case, the simulation study yielded a 3D error of 0.82 mm ± 0.24 mm (mean ± standard deviation) for 2D noise with a standard deviation of 1.232 mm (4 detector pixels). The experimental result for this view configuration obtained on an AXIOM Artis C-arm (Siemens AG, Healthcare Sector, Forchheim, Germany) system was 0.98 mm ± 0.29 mm, respectively.These results show that state-of-the-art C-arm systems can localize instruments with millimeter accuracy, and that they can accomplish this almost as well as an idealized theoretical counterpart. High stereotactic localization accuracy, good patient access, and CT-like 3D imaging capabilities render state-of-the-art C-arm systems ideal devices for X-ray based minimally invasive procedures.

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Section: Methodsmentioning

confidence: 99%

Accuracy of x-ray image-based 3D localization from two C-arm views: a comparison between an ideal system and a real device

et al. 2009

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“…By connecting the known 3D coordinates of beads to the identified corresponding 2D coordinates, P j can be computed. [18][19][20] Figure 1 shows identified beads of the PDS-2 phantom in a projection. The PDS-2 phantom was positioned so that the helix of beads in a projection image does not self-overlap.…”

Section: A1 Calibration Of Projection Matricesmentioning

confidence: 99%

Fiducial marker-based correction for involuntary motion in weight-bearing C-arm CT scanning of knees. II. Experiment

et al. 2014

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Purpose: A C-arm CT system has been shown to be capable of scanning a single cadaver leg under loaded conditions by virtue of its highly flexible acquisition trajectories. In Part I of this study, using the 4D XCAT-based numerical simulation, the authors predicted that the involuntary motion in the lower body of subjects in weight-bearing positions would seriously degrade image quality and the authors suggested three motion compensation methods by which the reconstructions could be corrected to provide diagnostic image quality. Here, the authors demonstrate that a flat-panel angiography system is appropriate for scanning both legs of subjects in vivo under weight-bearing conditions and further evaluate the three motion-correction algorithms using in vivo data. Methods: The geometry of a C-arm CT system for a horizontal scan trajectory was calibrated using the PDS-2 phantom. The authors acquired images of two healthy volunteers while lying supine on a table, standing, and squatting at several knee flexion angles. In order to identify the involuntary motion of the lower body, nine 1-mm-diameter tantalum fiducial markers were attached around the knee. The static mean marker position in 3D, a reference for motion compensation, was estimated by back-projecting detected markers in multiple projections using calibrated projection matrices and identifying the intersection points in 3D of the back-projected rays. Motion was corrected using three different methods (described in detail previously): (1) 2D projection shifting, (2) 2D deformable projection warping, and (3) 3D rigid body warping. For quantitative image quality analysis, SSIM indices for the three methods were compared using the supine data as a ground truth. Results: A 2D Euclidean distance-based metric of subjects' motion ranged from 0.85 mm (±0.49 mm) to 3.82 mm (±2.91 mm) (corresponding to 2.76 to 12.41 pixels) resulting in severe motion artifacts in 3D reconstructions. Shifting in 2D, 2D warping, and 3D warping improved the SSIM in the central slice by 20.22%, 16.83%, and 25.77% in the data with the largest motion among the five datasets (SCAN5); improvement in off-center slices was 18.94%, 29.14%, and 36.08%, respectively. Conclusions: The authors showed that C-arm CT control can be implemented for nonstandard horizontal trajectories which enabled us to scan and successfully reconstruct both legs of volunteers in weight-bearing positions. As predicted using theoretical models, the proposed motion correction methods improved image quality by reducing motion artifacts in reconstructions; 3D warping performed better than the 2D methods, especially in off-center slices.

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“…For each view, 3D projection lines from source to object to detector were determined from the position of the object's shadow on the fluoroscopic image and the physical gantry parameters. The intersection of the 3D lines provided the 3D estimate of the object's position (17). Back projection was used in stand-alone fashion during analysis of the simulated ablation study data to generate ground truth locations of the mapping/distal electrode of the mapping catheter and the pacing electrodes.…”

Section: Experimental Data Collectionmentioning

confidence: 99%