{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T17:06:57Z","timestamp":1789492017280,"version":"build-2803163510"},"reference-count":38,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T00:00:00Z","timestamp":1779062400000},"content-version":"vor","delay-in-days":17,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["KY2030000197"],"award-info":[{"award-number":["KY2030000197"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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\u2010related artifacts when the scanned object is not fully covered by the field of view (FOV).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This work aims to develop a Dual\u2010Domain Deep learning\u2010based method for CBCT Reconstruction from Truncated projections (D3CRT) through the guidance of non\u2010truncated prior information.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The D3CRT comprised sequential procedures in both projection and image domains. First, in projection domain, a Sinogram Generation Network (SG\u2010Net) based on the denoising diffusion probabilistic model (DDPM) was employed to predict the missing projection data outside the FOV. The SG\u2010Net was fine\u2010tuned via transfer learning using non\u2010truncated prior data to achieve object\u2010specific adaptation. FDK reconstruction was subsequently performed using the predicted projections. Second, in image domain, an Image Enhancement Network (IE\u2010Net) 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\u2010Net for final image quality enhancement. In\u2010vivo small animal experiments were conducted on a micro\u2010CBCT system to validate the D3CRT method, with non\u2010truncated prior data obtained from large\u2010FOV low\u2010resolution scans. Dice similarity coefficient (DSC), structural similarity index measure (SSIM), root mean square error (RMSE) were used for quantitative evaluation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The proposed D3CRT effectively improves the image reconstruction quality under truncated projection conditions. For whole\u2010body and lung regions, D3CRT achieved DSCs of 97.1% and 96.0%, outperforming the low\u2010resolution 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 and an SSIM of 98.1% for D3CRT, demonstrating better performance than the Low Resolution Image Constrained Reconstruction (LRICR) method which directly takes low\u2010resolution prior images as the initial inputs for CS reconstruction (RMSE 3.83, SSIM 97.4%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>By leveraging non\u2010truncated prior information and projection\u2010domain transfer learning, the proposed D3CRT effectively improved the overall quality of CBCT reconstruction from truncated projections.<\/jats:p>\n                  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