A competition-proven deep learning-based dual-domain framework for low-dose CBCT reconstruction

Background: Low-dose cone-beam computed tomography (CBCT) imaging reduces radiation exposure but inevitably increases noise levels, leading to degradation of reconstructed image quality. This study proposes and systematically evaluates a deep learning-based dual-domain framework, named DLDF, for improving CBCT image quality. The effectiveness of DLDF is demonstrated by its first-place ranking in the low-dose task of the ICASSP-2024 3D-CBCT Challenge.

Methods: The proposed DLDF integrates a projection domain model (PDNet), based on a three-dimensional (3D) Res-UNet architecture, with an image domain model (IDNet), based on a two-dimensional (2D) Res-UNet architecture. Low-dose projections were first denoised by PDNet, followed by Feldkamp-Davis-Kress (FDK) reconstruction, and then further refined by IDNet. The DLDF was trained on data from 800 patients, validated on 100 patients, and tested on 110 patients. On the validation set, the DLDF was compared with 10 state-of-the-art (SOTA) deep learning methods using five quantitative metrics: mean squared error (MSE), peak signal-to-noise ratio (PSNR), feature similarity (FSIM), structural similarity (SSIM), and visual information fidelity (VIF). Statistical analysis was conducted using the Friedman test, followed by the post-hoc Durbin-Conover test with Holm-Bonferroni correction. On the test set, the DLDF was compared against 8 competing methods submitted by other participants.

Results: The proposed DLDF preserved anatomical structures and fine details with notable improvements in overall visual integrity, while also demonstrating improved performance across quantitative metrics. On the validation set, the DLDF achieved average MSE, PSNR, FSIM, SSIM, and VIF values of 0.00162, 35.54 dB, 0.9709, 0.9186, and 0.8492, respectively, outperforming all 10 SOTA methods with statistically significant improvements across all metrics (P<0.05). On the test set, the DLDF achieved an average MSE of 0.00145, ranking first among all competing methods submitted by the participants.

Conclusions: