In this study, we present a 3D-printing based realistic anthropomorphic dental phantom and its imaging evaluation. A real skull phantom was scanned with MDCT at high resolution, and then image segmentation and 3D model were carried out. The created phantom was scanned with by using an MDCT and a dental CT scanner for image quality evaluation of metal artifacts. Our study demonstrated the feasibility of making 3D printing-based making realistic anthropomorphic phantoms which can be used in various dental imaging studies.
In this study, we present a 3D-printing based realistic anthropomorphic dental phantom and its imaging evaluation. A real skull phantom was scanned with CBCT with high resolution, and then image segmentation and 3D modeling were carried out for bones, teeth, and soft tissue. Followed by was 3D printing of bones and teeth with gypsum, with additional 3 teeth being printed with metal separately. For soft tissue, a negative model was first printed with PMMA, and then silicon gel was casted into the negative model with printed bones and teeth set in place. The created phantom was scanned with by using an MDCT and dental CBCT scanner for image quality evaluation of CT images, panoramic images, and also metal artifacts. Our goal was to make our phantom to mimic the real skull phantom. Our proposed phantom’s CT, panoramic images look almost the same with the real skull phantom’s one. Mean HU of bone was comparable between 3D printed and real skull phantoms (1860 vs 1730), and mean HU of soft tissue was 40 in 3D printed dental phantom. Image quality of dental CT images assessed by an expert was comparable between real skull and 3D printed phantom. Especially, the metal artifacts from the metal printed teeth was rated as realistically mimicking the real crowned teeth. Our study demonstrated the feasibility of making 3D printing-based making realistic anthropomorphic phantoms which can be used in various dental imaging studies.
Effective elimination of unique CT noise pattern while preserving adequate image quality is crucial in reducing radiation dose to ultra-low-dose level in CT imaging practice. In this study, we present a novel Deep Learning-enable Iterative Reconstruction (Deep IR) approach for CT denoising which incorporate a synthetic sinogram-based noise simulation technique for training of Convolutional Neural Network (CNN). Regular dose CT images from 25 patients were used from Seoul National University Hospital. The CT scans were performed at 140 kVp, 100 mAs, and reconstructed with standard FBP technique using B60f kernel. Among them, 20 patients were randomly selected as a training set and the rest 5 patients were used for a test set. We applied a re-projection technique to create a synthetic sinogram from the DICOM CT image, and then a simulated noise sinogram was generated to match the noise level of 10mAs according to Poisson statistic and the system noise model of the given scanner (Somatom Sensation 16, Siemens). We added the simulated noise sinogram to the re-projected synthetic sinogram to generate a simulated sinogram of ultra-low dose scan. We also created the simulated ultra-low-dose CT image by applying FBP reconstruction of the simulated noise sinogram with B60f kernel. A CNN model was created using a TensorFlow framework to have 10 consecutive convolution layer and activation layer. The CNN was trained to learn the noise in sinogram domain: the simulated noisy sinogram of ultra-low dose scan was fed into its input nodes with the output node being fed by the simulated noise sinogram. At test phase, the noise sinogram from the CNN output was reconstructed with using B60f kernel to create a noise CT image, which in turn was subtracted from the simulated ultra-low-dose CT image to produce a Deep IR CT image. The performance was evaluated quantitatively in terms of structural similarity (SSIM) index, peak signal-to-noise ratio (PSNR) and noise level measurement and qualitatively in CT image by comparing the noise pattern and image quality. Compared to low-dose image, denoising image of the SSIM and the PSNR were improved from 0.75 to 0.80, 28.61db to 32.16 respectively. The noise level of denoising image was reduced to an average of 56 % of that of low-dose image. The noise pattern in reconstructed noise CT was indistinguishable from that of real CT images, and the image quality of Deep IR CT image was overall much higher than that of simulated ultra-low-dose CT.