SynNet3D: multi-view projection synthesis model for sparse-view CBCT reconstruction

This study proposes a multi-view projection synthesis model, named SynNet3D, to improve the quality of sparse-view low-dose cone-beam computed tomography (CBCT) images. Based on an attention 3D Res-UNet architecture, the proposed SynNet3D model generated projections of missing views in a single stage. The full-view projections were restored from the 1/4 sparse-view projections using the SynNet3D method, and subsequently reconstructed using the Feldkamp-Davis-Kress (FDK) technique. Quantitative metrics includes pixel-level evaluation metrics, such as root-mean-square error (RMSE) and peak signal-to-noise ratio (PSNR). It also includes two metrics for evaluating the perceptual quality of images from the perspective of the human visual system, such as structural similarity (SSIM) and feature similarity (FSIM). On the test set, the SynNet3D model achieved averaged RMSE, PSNR, SSIM and FSIM values of 0.0416, 43.4 dB, 0.975 and 0.993 in the image domain, respectively. The SynNet3D model outperformed the existing single-view projection synthesis model, SynCNN, across all metrics. In conclusion, the SynNet3D model can effectively enhance the performance of sparse-view CBCT images.