Self-calibration of a cone-beam micro-CT system

Use of cone-beam computed tomography (CBCT) is becoming more frequent. For proper reconstruction, the geometry of the CBCT systems must be known. While the system can be designed to reduce errors in the geometry, calibration measurements must still be performed and corrections applied. Investigators have proposed techniques using calibration objects for system calibration. In this study, the authors present methods to calibrate a rotary-stage CB micro-CT (CBμCT) system using only the images acquired of the object to be reconstructed, i.e., without the use of calibration objects. Projection images are acquired using a CBμCT system constructed in the authors' laboratories. Dark- and flat-field corrections are performed. Exposure variations are detected and quantified using analysis of image regions with an unobstructed view of the x-ray source. Translations that occur during the acquisition in the horizontal direction are detected, quantified, and corrected based on sinogram analysis. The axis of rotation is determined using registration of antiposed projection images. These techniques were evaluated using data obtained with calibration objects and phantoms. The physical geometric axis of rotation is determined and aligned with the rotational axis (assumed to be the center of the detector plane) used in the reconstruction process. The parameters describing this axis agree to within 0.1 mm and 0.3 deg with those determined using other techniques. Blurring due to residual calibration errors has a point-spread function in the reconstructed planes with a full-width-at-half-maximum of less than 125 μm in a tangential direction and essentially zero in the radial direction for the rotating object. The authors have used this approach on over 100 acquisitions over the past 2 years and have regularly obtained high-quality reconstructions, i.e., without artifacts and no detectable blurring of the reconstructed objects. This self-calibrating approach not only obviates calibration runs, but it also provides quality control data for each data set.