Variational Synthesis Network for Generating Micro Computed Tomography from Cone Beam Computed Tomography

The cross-modality generation is usually a challenging task, especially for the medical image with a huge distribution difference. Micro Computed Tomography (Micro-CT) has high resolution and sharp contrast, which is regarded as the gold standard of tooth structure in dentistry. But Micro-CT has to scan a single isolated tooth in vitro, it cannot provide any information before surgery. Cone Beam Computed Tomography (CBCT) is commonly used in clinical treatment, but its low resolution and blurred border often lead to the deficiency of accurate tooth structure for surgery, such as the pulp and crown. The modality conversion from CBCT to Micro-CT is very difficult due to the huge difference between two modality distributions. Most GAN-based models cannot cope with such distribution differences between modalities. Therefore, we propose a novel cross-modality method for generating Micro-CT from CBCT. First, we propose a variation-based distance to reflect the difference between the two modality distributions. Second, integrating the variation-based distance, we design an end-to-end neural network named Variational Synthesis network (VSnet) to generate the Micro-CT from CBCT. Finally, we compare our method with plentiful state-of-the-art image generation methods. The results show that our proposed method is superior to other existing methods on the Micro-CT generation task, with image quality index SSIM of 0.912 $(\pm 0.061)$, PSNR of 22.451 $(\pm 4.810)$, and NRMSE of 0.261 $(\pm 0.142)$. In terms of the generation of the tooth structure, we also achieve superior results in clinic application. For the Micro-CT generated by VSnet, it is easy to achieve precise segmentations of tooth pulp and crown through simple thresholding, which facilitates the diagnosis in dental clinic.