A dual domain reconstruction network for sparse-view spectral CT integrating SSM and cross attention

Objective Sparse View Computed Tomography (SVCT) is an effective way to reduce radiation dose. However, missing information of projection leads to noise and artifacts. Current SVCT reconstruction networks cannot effectively utilize the correlation information of images from different energies, and linear interpolation in the projection domain brings more secondary artifacts. A dual-domain approach involving data complementation and correction is proposed as a potential solution in this work. Approach We introduce the MHSG-Model for sparse-view spectral CT, designed for integrated processing in both the projection and image domains. This model consists of a Projection Domain Generative Model (PDGM) and an Image Domain Correction Module (IDCM). The PDGM employs a generative model integrated with a selective State Space Model (SSM) to recover missing projection data, reducing secondary artifacts and computational complexity. The IDCM utilizes a novel cross-attention mechanism, incorporating a multi-scale attention for high-energy images and a high-frequency attention for low-energy images, to further enhance reconstruction quality. Main results The MHSG-Model was launched and validated on an abdominal slice simulation dataset and a sparse view spectral CT dataset of the AAPM DL Challenge. The proposed network achieved superior performance metrics, with an RMSE of 0.0111, MAE of 15.91, SSIM of 0.9674, and PSNR of 39.73 dB. The experimental results demonstrate that MHSG-Model has a good performance on preserving detail information and removing artifacts, which show the potential to be applied in clinical sparse view spectral CT reconstruction.