[A segmented backprojection tensor degradation feature encoding model for motion artifacts correction in dental cone beam computed tomography].

Abstract

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.