A simple and precise alignment calibration method for cone-beam computed tomography with the verifications.

Cone-beam Computed Tomography (CBCT) is widely used in dental imaging, small animal imaging, radiotherapy, and non-destructive industrial inspection. The quality of CBCT images depends on the precise knowledge of the CBCT system's alignment. We introduce a distinct procedure, "precision alignment loop (PAL)", to calibrate any CBCT system with a circular trajectory. We describe the calibration procedure by using a line-beads phantom, and how PAL determines the misalignments from a CBCT system. PAL also yields the uncertainties in the simulated calibration to give an estimate of the errors in the misalignments. From the analytical simulations, PAL can precisely obtain the source-to-rotation axis distance (SRD), and the geometric center G, "the point in z-axis meets the detector", where the z-axis is coincident with the line from the X-ray source that intersects the axis of the rotation (AOR) orthogonally. The uncertainties of three misalignment angles of the detector are within ±0.05°, which is close to ±0.04° for the results of Yang et al. [18], but our method is easy and simple to implement. Our distinct procedure, on the other hand, yields the calibration of a micro-CT system and an example of reconstructed images, showing our calibration method for the CBCT system to be simple, precise, and accurate.

Cone-beam x-ray luminescence computed tomography (CB-XLCT) prototype development and performance evaluation.

This study developed a prototype for a rotational cone-beam x-ray luminescence computed tomography (CB-XLCT) system, considering its potential application in pre-clinical theranostic imaging. A geometric calibration method applicable to both imaging chains (XL and CT) was also developed to enhance image quality. The results of systematic performance evaluations were presented to assess the feasibility of commercializing XLCT technology. Monte Carlo GATE simulation was performed to determine the optimal imaging conditions for nanophosphor particles (NPs) irradiated by 70 kV x-rays. We acquired a low-dose transmission x-ray tube and designed a prone positioning platform and a rotating gantry, using mice as targets from commercial small animalµ-CT systems. We then employed the image cross-correlation (ICC) automatic geometric calibration method to calibrate XL and CT images. The performance of the system was evaluated through a series of phantom experiments with a linearity of 0.99, and the contrast-to-noise ratio (CNR) between hydroxyl-apatite (HA) and based epoxy resin is 19.5. The XL images of the CB-XLCT prototype achieved a Dice similarity coefficient (DICE) of 0.149 for a distance of 1 mm between the two light sources. Finally, the final XLCT imaging results were demonstrated using the Letter phantoms with NPs. In summary, the CB-XLCT prototype developed in this study showed the potential to achieve high-quality imaging with acceptable radiation doses for small animals. The performance of CT images was comparable to current commercial machines, while the XL images exhibited promising results in phantom imaging, but further efforts are needed for biomedical applications.

Virtual and real-world implementation of deep-learning-based image denoising model on projection domain in digital tomosynthesis and cone-beam computed tomography data.

Reducing the radiation dose will cause severe image noise and artifacts, and degradation of image quality will also affect the accuracy of diagnosis. To find a solution, we comprise a 2D and 3D concatenating convolutional encoder-decoder (CCE-3D) and the structural sensitive loss (SSL), via transfer learning (TL) denoising in the projection domain for low-dose computed tomography (LDCT), radiography, and tomosynthesis. The simulation and real-world practicing results show that many of the figures-of-merit (FOMs) increase in both projections (2-3 times) and CT imaging (1.5-2 times). From the PSNR and structural similarity index of measurement (SSIM), the CCE-3D model is effective in denoising but keeps the shape of the structure. Hence, we have developed a denoising model that can be served as a promising tool to be implemented in the next generation of x-ray radiography, tomosynthesis, and LDCT systems.