CBCT Tooth Images Super-Resolution Method Based on GAN Prior

Aiming at the problem that high-resolution cone-beam computed tomography images are difficult to obtain, a cone-beam computed tomography image super-resolution method based on prior generative adversarial network is proposed, which uses the micro computed tomography image as a reference to perform weakly supervised training on the super-resolution network. Firstly, a generative adversarial network is trained to generate high-quality micro computed tomography images of a single tooth, which is embedded into a U-shaped backbone network as a prior decoder. Then, low-resolution cone-beam computed tomography images of multiple teeth are used to train the backbone network, and the network is first located to each tooth in the cone-beam computed tomography image. Finally, the domain gap between cone-beam computed tomography and micro computed tomography is solved. By designing a domain adaptive degradation module based on wavelet transform noise extraction, the generator is indirectly optimized to generate images more in line with the information distribution of micro computed tomography. The experimental results on the cone-beam computed tomography dataset show that compared with the existing super-resolution methods, the proposed method improves the peak signal-to-noise ratio by 0.79-6.02 dB, reduces the learned perceptual image patch similarity by 0.01-0.72, and has better visual effect in the tooth super-resolution, which has strong competitiveness.