The interior reconstruction problem in CT has been a long-standing challenge in medical ima-ging.Due to the small field of view during acquisition,truncation artifacts may appear on CT images,signifi-cantly reducing image quality and impacting diagnosis.Therefore,we propose a dual-domain transformation en-coding-decoding network model(Dual-domain U-Net).This model can simultaneously adjust parameters in both the projection domain and the image domain,enhancing the robustness and fidelity of the model.We validated the effectiveness of the model on 20 public datasets from the Cancer Imaging Archive(TCIA)of the American Association of Physicists in Medicine(AAPM)and the National Cancer Institute.Experimental results demon-strate that our proposed model can remove truncation artifacts on CT images and exhibit superior performance in preserving image details.
维普
万方数据