Cone-beam x-ray luminescence computed tomography (CB-XLCT) prototype development and performance evaluation.
Yu-Hong WangDavid Shih-Chun JinTian-Yu WuChieh ShenJyh-Cheng ChenSnow H TsengTse-Ying LiuPublished in: Physics in medicine and biology (2024)
This study developed a prototype of 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. In terms of development, we first used Monte Carlo GATE simulation to determine the optimal imaging conditions for nanophosphor particles (NPs). Based on the simulation results and the imaging conditions of mice, we acquired a low-dose transmission X-ray tube and designed a prone positioning platform and a rotating gantry, taking reference from commercial small animal μ-CT systems. We then employed 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. The CB-XLCT prototype demonstrated a linearity performance of 0.99 in CT images, a contrast of 19.5, and a spatial resolution of 0.077 mm. The XL images of the CB-XLCT prototype achieved a Dice similarity coefficient (DICE) of 0.149 and a peak signal-noise-ratio (PSNR) of 46.897 for a distance of 1 mm between the two light sources. Finally, the final XLCT imaging results were demonstrated using 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.
Keyphrases
- dual energy
- image quality
- computed tomography
- high resolution
- deep learning
- contrast enhanced
- positron emission tomography
- low dose
- magnetic resonance imaging
- convolutional neural network
- machine learning
- type diabetes
- fluorescence imaging
- high dose
- adipose tissue
- air pollution
- mass spectrometry
- cone beam
- metabolic syndrome
- drinking water