OBJECTIVES: We propose a segmented backprojection tensor degradation feature encoding (SBP-MAC) model for motion artifact correction in dental cone beam computed tomography (CBCT) to improve the quality of the reconstructed images. METHODS: The proposed motion artifact correction model consists of a generator and a degradation encoder. The segmented limited-angle reconstructed sub-images are stacked into the tensors and used as the model input. A degradation encoder is used to extract spatially varying motion information in the tensor, and the generator's skip connection features are adaptively modulated to guide the model for correcting artifacts caused by different motion waveforms. The artifact consistency loss function was designed to simplify the learning task of the generator. RESULTS: The proposed model could effectively remove motion artifacts and improve the quality of the reconstructed images. For simulated data, the proposed model increased the peak signal-to-noise ratio by 8.28%, increased the structural similarity index measurement by 2.29%, and decreased the root mean square error by 23.84%. For real clinical data, the proposed model achieved the highest expert score of 4.4221 (against a 5-point scale), which was significantly higher than those of all the other comparison methods. CONCLUSIONS The SBP-MAC model can effectively extract spatially varying motion information in the tensors and achieve adaptive artifact correction from the tensor domain to the image domain to improve the quality of reconstructed dental CBCT images.
Cone-Beam Computed Tomography/methods* ; Artifacts ; Humans ; Motion ; Image Processing, Computer-Assisted/methods* ; Signal-To-Noise Ratio ; Algorithms

Objective We propose a motion artifact correction algorithm(DMBL)for reducing motion artifacts in reconstructed dental cone-beam computed tomography(CBCT)images based on deep blur learning.Methods A blur encoder was used to extract motion-related degradation features to model the degradation process caused by motion,and the obtained motion degradation features were imported in the artifact correction module for artifact removal.The artifact correction module adopts a joint learning framework for image blur removal and image blur simulation for treatment of spatially varying and random motion patterns.Comparative experiments were conducted to verify the effectiveness of the proposed method using both simulated motion data sets and clinical data sets.Results The experimental results with the simulated dataset showed that compared with the existing methods,the PSNR of the proposed method increased by 2.88%,the SSIM increased by 0.89%,and the RMSE decreased by 10.58%.The results with the clinical dataset showed that the proposed method achieved the highest expert level with a subjective image quality score of 4.417(in a 5-point scale),significantly higher than those of the comparison methods.Conclusion The proposed DMBL algorithm with a deep blur joint learning network structure can effectively reduce motion artifacts in dental CBCT images and achieve high-quality image restoration.

Objective We propose a motion artifact correction algorithm(DMBL)for reducing motion artifacts in reconstructed dental cone-beam computed tomography(CBCT)images based on deep blur learning.Methods A blur encoder was used to extract motion-related degradation features to model the degradation process caused by motion,and the obtained motion degradation features were imported in the artifact correction module for artifact removal.The artifact correction module adopts a joint learning framework for image blur removal and image blur simulation for treatment of spatially varying and random motion patterns.Comparative experiments were conducted to verify the effectiveness of the proposed method using both simulated motion data sets and clinical data sets.Results The experimental results with the simulated dataset showed that compared with the existing methods,the PSNR of the proposed method increased by 2.88%,the SSIM increased by 0.89%,and the RMSE decreased by 10.58%.The results with the clinical dataset showed that the proposed method achieved the highest expert level with a subjective image quality score of 4.417(in a 5-point scale),significantly higher than those of the comparison methods.Conclusion The proposed DMBL algorithm with a deep blur joint learning network structure can effectively reduce motion artifacts in dental CBCT images and achieve high-quality image restoration.