The quality of Computed Tomography (CT) images crucially depends on the precise knowledge of the scanner geometry. Therefore, it is necessary to estimate and calibrate the misalignments before image acquisition. In this paper, a Two-Piece-Ball (TPB) phantom is used to estimate a set of parameters that describe the geometry of a cone-beam CT system. Only multiple projections of the TPB phantom at one position are required, which can avoid the rotation errors when acquiring multi-angle projections. Also, a corresponding algorithm is derived. The performance of the method is evaluated through simulation and experimental data. The results demonstrated that the proposed method is valid and easy to implement. Furthermore, the experimental results from the Micro-CT system demonstrate the ability to reduce artifacts and improve image quality through geometric parameter calibration.

Micro-CT with a high spatial resolution in combination with computer-based-reconstruction techniques is considered a powerful tool for morphological study of insects. The quality of CT images crucially depends on the precise knowledge of the scan geometry of the micro-CT system. In this paper, we have proposed a method to calculate the deviation of rotating axis for compensating deficiency of existing methods. A practical application of this geometric calibration method of the micro-CT system for insect imaging is presented. We have performed the computer-simulation study and experimental study with our prototype micro-CT system. The results demonstrate that the proposed technique is accurate and robust. In addition, we have evaluated the imaging characteristics of the detector in terms of modulation-transfer function (MTF). Finally, insect imaging performance and image reconstruction from data acquired with different energies are presented.

Dual-energy computed tomography has a better material determination capability than traditional CT. In this paper, we develop a dual-layer dual-energy micrometer computed tomography (DEMCT) imaging system with micrometer spatial resolution. The detector of the DEMCT system uses GAGG: Ce and LYSO: Ce scintillators to absorb x-rays of different energies, which is a new application of GAGG: Ce scintillator. As GAGG: Ce and LYSO: Ce have high light yields and their emission wavelengths match well to the quantum efficiency of common CCD, thicknesses of the scintillators are allowed to be thin, which is of great significance for high resolution imaging due to the limitation of the depth of field of objectives. We propose a mechanical adjustment method for registering high-energy and low-energy images, which saves the post-processing time and avoids oscillating or non-convergent iteration results of mathematical registration methods. Meanwhile, in order to increase the dual-energy spectra separation of materials detection in the DEMCT, a fast optimal kVp selection method is proposed. Results show that the DEMCT has the spatial resolution of 2 micrometers in low-energy imaging and 4 micrometers in high-energy imaging. In addition, materials of a silica mixture sample are clearly separated by the pixel selection of the energy map based on the DEMCT data. The proposed DEMCT system has the potential to separate materials that cannot be correctly determined on traditional CT.

Linear Computed Laminography (LCL) is used to yield slice images of plate-like objects (PLO) for the advantage of short exposure time, high control precision and low cost. Shift and Add (SAA) is a widely used reconstruction algorithm for LCL. One limitation of SAA is that the reconstructed image of the in-focus layer (IFL) contains information from off-focus layers (OFL), resulting in inter-slice aliasing and blurring. In this paper, an Iterative Difference Deblurring (IDD) algorithm based on LCL is proposed to reduce the blur in reconstructed images. The core idea of the IDD algorithm is: contributions from OFL are subtracted from the projection data to remove the blur from the IFL. The corrected projections are then reconstructed using the SAA to remove the superimposed contributions of OFL from the IFL. An iterative approach is utilized to adjust a weighting factor applied during the subtraction stage. The results demonstrate that IDD algorithm can achieve PLO reconstruction in the LCL system under extremely sparse sampling conditions, and can effectively reduce the inter-slice aliasing and blurring.