Purpose: In recent years, health risks concerning high-dose x-ray radiation have become a major concern in dental computed tomography (CT) examinations. Therefore, adopting low-dose computed tomography (LDCT) technology has become a major focus in the CT imaging field. One of these LDCT technologies is downsampling data acquisition during low-dose x-ray imaging processes. However, reducing the radiation dose can adversely affect CT image quality by introducing noise and artifacts in the resultant image that can compromise diagnostic information. In this paper, we propose an artifact correction method for downsampling CT reconstruction based on deep learning. Method: We used clinical dental CT data with low-dose artifacts reconstructed by conventional filtered back projection (FBP) as inputs to a deep neural network and corresponding high-quality labeled normal-dose CT data during training. We trained a generative adversarial network (GAN) with Wasserstein distance (WGAN) and mean squared error (MSE) loss, called m-WGAN, to remove artifacts and obtain high-quality CT dental images in a clinical dental CT examination environment. Results: The experimental results confirmed that the proposed algorithm effectively removes lowdose artifacts from dental CT scans. In addition, we showed that the proposed method is efficient for removing noise from low-dose CT scan images compared to existing approaches. We compared the performances of the general GAN, convolutional neural networks, and m-WGAN. Through quantitative and qualitative analysis of the results, we concluded that the proposed m-WGAN method resulted in better artifact correction performance preserving the texture in dental CT scanning. Conclusions: The image quality evaluation metrics indicated that the proposed method effectively improves image quality when used as a postprocessing technique for dental CT images. To the best of our knowledge, this work is the first deep learning architecture used with a commercial cone-beam dental CT scanner. The artifact correction performance was rigorously evaluated and demonstrated to be effective. Therefore, we believe that the proposed algorithm represents a new direction in the research area of low-dose dental CT artifact correction.

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In this work, a small animal PET scanner named SIAT aPET was developed using dual-ended readout depth encoding detectors to simultaneously achieve high spatial resolution and high sensitivity. The scanner consists of four detector rings with 12 detector modules per ring; the ring diameter is 111 mm and the axial field of view (FOV) is 105.6 mm. The images are reconstructed using an ordered subset expectation maximization (OSEM) algorithm. The spatial resolution of the scanner was measured by using a 22Na point source at the center axial FOV with different radial offsets. The sensitivity of the scanner was measured at center axis of the scanner with different axial positions. The count rate performance of the system was evaluated by scanning mouse-sized and rat-sized phantoms. An ultra-micro hot-rods phantom and two mice injected with 18F-NaF and 18F-FDG were scanned on the scanner. An average depth of interaction (DOI) resolution of 1.96 mm, energy resolution of 19.1% and timing resolution of 1.20 ns were obtained for the detector. Average spatial resolutions of 0.82 mm and 1.16 mm were obtained up to a distance of 30 mm radially from the center of the FOV when reconstructing a point source in 1% and 10% warm backgrounds, respectively, using OSEM reconstruction with 16 subsets and 10 iterations. Sensitivities of 16.0% and 11.9% were achieved at center of the scanner for energy windows of 250–750 keV and 350–750 keV respectively. Peak noise equivalent count rates (NECRs) of 324 kcps and 144 kcps were obtained at an activity of 26.4 MBq for the mouse-sized and rat-sized phantoms. Rods of 1.0 mm diameter can be visually resolved from the image of the ultra-micro hot-rods phantom. The capability of the scanner was demonstrated by high quality in-vivo mouse images.

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