Trans2-CBCT: A Dual-Transformer Framework for Sparse-View CBCT Reconstruction

Abstract

Cone-beam computed tomography (CBCT) with sparse projection views offers reduced radiation dose and faster scans but introduces severe streak artifacts and spatial coverage gaps. We address these challenges within a unified framework. First, we replace conventional UNet/ResNet encoders with TransUNet, a hybrid CNN–Transformer architecture that jointly models local details and long-range spatial context. It is adapted to CBCT reconstruction by concatenating multi-scale feature maps and introducing a lightweight attenuation-prediction head. Trans-CBCT outperforms the best baseline by 1.17 dB in PSNR and by 0.0163 in SSIM on LUNA16 with only six projection views. Second, we incorporate a neighbor-aware Point Transformer with explicit 3D positional encodings and a neighbor-aware attention module aggregating information from each point’s k-nearest spatial neighbors to enforce volumetric coherence. The resulting Trans2-CBCT achieves an additional 0.63 dB increase in PSNR and 0.0117 increase in SSIM over Trans-CBCT. In experiments with 6-10 views, Trans-CBCT and Trans2-CBCT consistently outperform all prior methods in both PSNR and SSIM on LUNA16. On the ToothFairy dataset, Trans2-CBCT leads in five of the six measurements, outperforming all baselines in PSNR. These results highlight the effectiveness of combining hybrid CNN–Transformer features with geometry-aware point-based reasoning for sparse-view CBCT reconstruction.