Dual-domain sparse-view CT reconstruction with Transformers

Purpose: Computed Tomography (CT) has been widely used in the medical field. Sparse-view CT is an effective and feasible method to reduce the radiation dose. However, the conventional filtered back projection (FBP) algorithm will suffer from severe artifacts in sparse-view CT. Iterative reconstruction algorithms have been adopted to remove artifacts, but they are time-consuming due to repeated projection and back projection and may cause blocky effects. To overcome the difficulty in sparse-view CT, we proposed a dual-domain sparse-view CT algorithm CT Transformer (CTTR) and paid attention to sinogram information. Methods: CTTR treats sinograms as sentences and enhances reconstructed images with sinogram's characteristics. We qualitatively evaluate the CTTR, an iterative method TVM-POCS, a convolutional neural network based method FBPConvNet in terms of a reduction in artifacts and a preservation of details. Besides, we also quantitatively evaluate these methods in terms of RMSE, PSNR and SSIM. Results: We evaluate our method on the Lung Image Database Consortium image collection with different numbers of projection views and noise levels. Experiment studies show that, compared with other methods, CTTR can reduce more artifacts and preserve more details on various scenarios. Specifically, CTTR improves the FBPConvNet performance of PSNR by 0.76 dB with 30 projections. Conclusions: The performance of our proposed CTTR is better than the method based on CNN in the case of extremely sparse views both on visual results and quantitative evaluation. Our proposed method provides a new idea for the application of Transformers to CT image processing.