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

Abstract

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.

Geometric calibration of a mobile C‐arm for intraoperative cone‐beam CT

“…Methods of pose characterization include the use of stereoscopic tracking systems to monitor mechanical motion 25 and, more commonly, image-based methods that operate directly on projection data acquired either from a prior calibration 2,18,23,24 or simultaneous with imaging. 20,28 The form of the geometric characterization can consist of a projection matrix describing the linear relationship between 3D voxel coordinates and 2D pixel coordinates, 19,24 or a set of geometric parameters describing degrees of freedom in the imaging system ͑e.g., source and detector positions, detector rotation angles, etc.͒. 18 Perhaps the simplest method for image-based geometric calibration of a CBCT system uses a single ball bearing ͑BB͒ placed near the isocenter of the rotational gantry to characterize the location of the "piercing point" ͑i.e., the point at which the isocenter projects on the detector plane͒ as a function of gantry angle.…”

“…2 In general, geometric calibration relates the 3D coordinates ͑x , y , z͒ of voxels in the reconstructed image to the 2D coordinates ͑u , v͒ of pixels in the projection domain. [18][19][20][21][22][23][24][25][26][27][28] Geometric calibration consists of two stages: ͑i.͒ characterization of pose across the range of source-detector orbit; and ͑ii.͒ correction of geometric nonidealities in the process of 3D reconstruction. Methods of pose characterization include the use of stereoscopic tracking systems to monitor mechanical motion 25 and, more commonly, image-based methods that operate directly on projection data acquired either from a prior calibration 2,18,23,24 or simultaneous with imaging.…”

Clinical evaluation of angiographic multiple-view 3D reconstruction

“…Points along 2D-vessel centerlines are manually indicated and fit using cubic splines [18]. In the current method, the points should be placed with sufficient frequency so that the straight-line segments connecting the points lie inside the vessel.…”

“…In a previous publication [18], we presented the multipleview self-calibration technique, which involves two steps: (1) calibration of the geometry, and (2) reconstruction of the 3D vessel centerline, as well as results of simulation and phantom studies. In simulation and phantom studies, we found that the average RMS difference between the pair-wise calculated 3D 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 (using our multiple-view self-calibration technique).…”

“…But, a sufficient number of corresponding points is not always available across the multiple views. Thus, we developed a technique for self-calibration of the imaging geometry and reconstruction of the vessel of interest from multiple projection views based on the centerline of a single vessel [18].…”