Analytic method based on identification of ellipse parameters for scanner calibration in cone-beam tomography

Abstract

This paper is about calibration of cone-beam (CB) scanners for both x-ray computed tomography and single-photon emission computed tomography. Scanner calibration refers here to the estimation of a set of parameters which fully describe the geometry of data acquisition. Such parameters are needed for the tomographic reconstruction step. The discussion is limited to the usual case where the cone vertex and planar detector move along a circular path relative to the object. It is also assumed that the detector does not have spatial distortions. We propose a new method which requires a small set of measurements of a simple calibration object consisting of two spherical objects, that can be considered as 'point' objects. This object traces two ellipses on the detector and from the parametric description of these ellipses, the calibration geometry can be determined analytically using explicit formulae. The method is robust and easy to implement. However, it is not fully general as it is assumed that the detector is parallel to the rotation axis of the scanner. Implementation details are given for an experimental x-ray CB scanner.

Software architecture for multi-bed FDK-based reconstruction in X-ray CT scanners

“…Many methods for the estimation of geometrical parameters of cone-beam scanners have been proposed since 1990 [20, [26][27][28][29][30]. These techniques have generally fallen into two categories: those based on iterative nonlinear optimization [26,27] and those based on the direct solution of geometric equations [20,28,29,31]. The second type has become favored in recent years because of superior performance and ease of implementation.…”

“…Contrarily, in [20,32] the authors propose methods that avoid strict requirements on the object shape and positioning. The latter only determines the transversal shift, the skew, and, less accurately, the detector slant; other parameters, such as detector tilt, longitudinal shift, and error in the source-detector distance, cannot be determined.…”

“…The method described in [20] was selected for our architecture because it is robust, easy to implement and uses a simple calibration object. Although the method is not fully general (as tilt angle is assumed to be equal zero  = 0), it provides the set of parameters we found relevant for our scanner [25].…”

“…Despite the existence of a remarkable progress in statistical reconstruction algorithms, approximate methods based on the algorithm are numerous additional details to consider in order to obtain good quality images at a reasonable computational cost. Many of these aspects have also been previously studied independently, for instance the characterization of the misalignments [20] or artifact corrections [21,22] but, to our best knowledge, there is no previous literature covering the complete process and detailing all the aspects to be considered: the whole workflow, the selection criteria for the different components and their right arrangement, has not been described previously. Nevertheless, when facing the design of a real scanner it is not a straightforward task to assess the optimum data workflow considering all the calibration, correction, and reconstruction issues.…”

Long‐term evaluation and cross‐checking of two geometric calibrations of kV and MV imaging systems for Linacs

“…Linear accelerators with integrated kilovoltage (kV) imaging have become routine clinical tools for IGRT and high precision of treatment is expected. It is critical to evaluate geometric accuracy and coincidence of kV imaging systems, 4 , 5 and also the megavoltage (MV) treatment beam, which are the baseline for limit machine uncertainty. In this study, two geometric calibration methodologies have been used to assess kV and MV cone‐beam imaging systems and their coincidence on two linear accelerators during one year.…”