A generic geometric calibration method for tomographic imaging systems with flat‐panel detectors—A detailed implementation guide

Purpose To present a generic geometric calibration method for tomographic imaging systems with flat‐panel detectors in a very detailed manner, in the aim to provide a useful tool to the public domain. Methods The method is based on a projection matrix which represents a mapping from 3D object coordinate system to 2D projection image plane. The projection matrix can be determined experimentally through the imaging of a phantom of known marker geometry. Accurate implementation was accomplished through direct computation algorithms, including a novel ellipse fitting using singular value decomposition and data normalization. Benefits of the method include: (1) It is capable of being applied to systems of different scan trajectories, source‐detector alignments, and detector orientations; (2) projection matrices can be utilized in image reconstructions or in the extraction of explicit geometrical parameters; and (3) the method imposes minimal limits on the design of calibration phantom. C++ programs that calculate projection matrices and extract geometric parameters from them are also provided. For validation, the calibration method was applied to the computer simulation of a cone‐beam CT system, as well as to three tomosynthesis prototypes of different source‐detector movement patterns: Source and detector rotating synchronizedly; source rotating and detector wobbling; and source rotating and detector staying stationary. Results Projection matrices were computed on a view by view basis. Geometric parameters extracted from projection matrices were consistent with actual settings. Images were reconstructed by directly using projection matrices, and were compared to virtual Shepp–Logan image for CT simulation and to central projection images of CIRS breast phantoms for tomosynthesis prototypes. They showed no obvious distortion or blurring, indicating the high quality of geometric calibration results. When the computed central ray offsets were perturbed with Gaussian noises of 1 pixel standard deviation, the reconstructed image showed apparent distortion, which further demonstrated the accuracy of the geometric calibration method. Conclusions The method is suitable for tomographic imaging systems with flat‐panel detectors.