Medical physics · 2026
Cone beam computed tomography reconstruction from truncated projections using prior information and transfer learning.
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Han Yiqun, Fang Chengyijue, Huang Yunwen, Wu Qiwei, Zheng Cheng, Liu Hui, Yang Yidong
Аннотация
BACKGROUND: Cone beam computed tomography (CBCT) is widely used in clinical practice and small animal research for image guidance. The reconstruction quality will be compromised by severe truncation-related artifacts when the scanned object is not fully covered by the field of view (FOV).
PURPOSE: This work aims to develop a Dual-Domain Deep learning-based method for CBCT Reconstruction from Truncated projections (D3CRT) through the guidance of non-truncated prior information.
METHODS: The D3CRT comprised sequential procedures in both projection and image domains. First, in projection domain, a Sinogram Generation Network (SG-Net) based on the denoising diffusion probabilistic model (DDPM) was employed to predict the missing projection data outside the FOV. The SG-Net was fine-tuned via transfer learning using non-truncated prior data to achieve object-specific adaptation. FDK reconstruction was subsequently performed using the predicted projections. Second, in image domain, an Image Enhancement Network (IE-Net) was applied to refine the FDK reconstructed images. Compressed sensing (CS) reconstruction was then carried out to enforce data fidelity by incorporating the original projections, followed by a secondary IE-Net for final image quality enhancement. In-vivo small animal experiments were conducted on a micro-CBCT system to validate the D3CRT method, with non-truncated prior data obtained from large-FOV low-resolution scans. Dice similarity coefficient (DSC), structural similarity index measure (SSIM), root mean square error (RMSE) were used for quantitative evaluation.
RESULTS: The proposed D3CRT effectively improves the image reconstruction quality under truncated projection conditions. For whole-body and lung regions, D3CRT achieved DSCs of 97.1% and 96.0%, outperforming the low-resolution prior images (DSCs of 96.8% and 89.9%) when compared with the reference region segmentations. Quantitative evaluations within the FOV yielded an average RMSE of 2.95 × 1 0 - 3 m m - 1 $ \times 1{{{\mathrm{0}}}^{{\mathrm{ - 3}}}}{\mathrm{m}}{{{\mathrm{m}}}^{{\mathrm{ - 1}}}}$ and an SSIM of 98.1% for D3CRT, demonstrating better performance than the Low Resolution Image Constrained Reconstruction (LRICR) method which directly takes low-resolution prior images as the initial inputs for CS reconstruction (RMSE 3.83 × 10 - 3 m m - 1 $ \times 10^{- 3}{\mathrm{m}}{\mathrm{m}}^{-1}$ , SSIM 97.4%).
CONCLUSION: By leveraging non-truncated prior information and projection-domain transfer learning, the proposed D3CRT effectively improved the overall quality of CBCT reconstruction from truncated projections.
PURPOSE: This work aims to develop a Dual-Domain Deep learning-based method for CBCT Reconstruction from Truncated projections (D3CRT) through the guidance of non-truncated prior information.
METHODS: The D3CRT comprised sequential procedures in both projection and image domains. First, in projection domain, a Sinogram Generation Network (SG-Net) based on the denoising diffusion probabilistic model (DDPM) was employed to predict the missing projection data outside the FOV. The SG-Net was fine-tuned via transfer learning using non-truncated prior data to achieve object-specific adaptation. FDK reconstruction was subsequently performed using the predicted projections. Second, in image domain, an Image Enhancement Network (IE-Net) was applied to refine the FDK reconstructed images. Compressed sensing (CS) reconstruction was then carried out to enforce data fidelity by incorporating the original projections, followed by a secondary IE-Net for final image quality enhancement. In-vivo small animal experiments were conducted on a micro-CBCT system to validate the D3CRT method, with non-truncated prior data obtained from large-FOV low-resolution scans. Dice similarity coefficient (DSC), structural similarity index measure (SSIM), root mean square error (RMSE) were used for quantitative evaluation.
RESULTS: The proposed D3CRT effectively improves the image reconstruction quality under truncated projection conditions. For whole-body and lung regions, D3CRT achieved DSCs of 97.1% and 96.0%, outperforming the low-resolution prior images (DSCs of 96.8% and 89.9%) when compared with the reference region segmentations. Quantitative evaluations within the FOV yielded an average RMSE of 2.95 × 1 0 - 3 m m - 1 $ \times 1{{{\mathrm{0}}}^{{\mathrm{ - 3}}}}{\mathrm{m}}{{{\mathrm{m}}}^{{\mathrm{ - 1}}}}$ and an SSIM of 98.1% for D3CRT, demonstrating better performance than the Low Resolution Image Constrained Reconstruction (LRICR) method which directly takes low-resolution prior images as the initial inputs for CS reconstruction (RMSE 3.83 × 10 - 3 m m - 1 $ \times 10^{- 3}{\mathrm{m}}{\mathrm{m}}^{-1}$ , SSIM 97.4%).
CONCLUSION: By leveraging non-truncated prior information and projection-domain transfer learning, the proposed D3CRT effectively improved the overall quality of CBCT reconstruction from truncated projections.