Low-dose single-photon emission computed tomography (SPECT) imaging can reduce the radiation damage to human bodies caused by radioactive tracers, and hence it is becoming more and more important in clinical practice. In a SPECT system, low-dose imaging can be achieved by acquiring projection data of sparse-view. The sparse-view projection data, if directly reconstructed by conventional iterative reconstruction methods, will inevitably lead to severe ray artifacts in the image domain. Existing clinical reconstruction methods usually introduce specific regularization to the optimization model to suppress ray artifacts. However, this type of methods may not adapt to projection data with various dosage, and the form of regularization heavily depends on prior knowledge. A novel neural network architecture is proposed to learn the mapping from the sparse-view projection data to the full-view projection data. The projection data of missing view angle is synthesized by the proposed neural network to improve the quality of reconstructed images. Numerical experiments show that, compared with the traditional iterative reconstruction method, the SSIM of the reconstructed image is increased by 59%, the NMSE is reduced by 67%, and the PSNR is increased by 2.48 dB. Therefore, the proposed method can better improve the image quality of sparse-view projection data.

The radioactive tracers used in Single-Photon Emission computerized Tomography (SPECT) scans can cause radiation exposure to the human body. Therefore, low-dose SPECT has attracted widespread attention in nuclear medicine imaging. Under low-dose imaging conditions, projection data is heavily contaminated by severe noise. There has been a significant amount of studies that explore fully supervised deep learning reconstruction methods to suppress image noise. The quality of images obtained by fully supervised methods depend on the quantity and quality of the labels. However, it is challenging to obtain the normal-dose images with supervised labels in clinical practice. To overcome the challenge, we propose a pre-trained mean teacher method with GAN loss to achieve low-dose SPECT reconstruction. The proposed method introduces a Swin-Conv-Unet-based pre-trained model into the mean teacher model to enhance the reliability of unlabeled training data. The teacher model supervises the student model through consistency regularization; the pre-trained model is trained with a small amount of labeled data and enhances the supervision reliability through GAN loss. Numerical experiments validate the performance of the proposed method in noise suppression and feature preservation. When compared with the mean teacher method, the SSIM of the reconstructed images is increased by 2%, the RMSE is reduced by 9%, and the PSNR is increased by 0.77 dB. The dataset is generated by the SIMIND simulation software using XCAT digital phantoms.

Sparse-view tomographic reconstruction is of significant importance for reducing radiation dose in clinical practice. In recent years, Implicit Neural Representation (INR) methods have been widely applied to medical image reconstruction in sparse-view scenario and have achieved competitive performance. However, traditional INR methods treat each sampling point individually as input, which neglect the inherent relations among neighboring sampling points, thus weakening the reconstruction performance. To address this, this paper proposes a novel INR method. The proposed method reorganizes neighboring sampling points on adjacent rays into multiple windows-of-interest, which are then fed into a Transformer query network equipped with a skip connection. By leveraging the self-attention mechanism of the Transformer network, the proposed method is able to capture the intrinsic relations among sampling points within each window-of-interest, thereby effectively enhancing the reconstructed image quality. This paper conducts extensive numerical experiments in two tomographic imaging modalities: Cone-Beam Computed Tomography (CBCT) and parallel-beam Single-Photon Emission Computed Tomography (SPECT) . The experimental results show that, compared to the advanced INR method Freq-NAF, the proposed method achieves superior performance in terms of reconstruction accuracy and image visualization under sparse-view conditions, particularly obtaining a 0.45 dB improvement in Peak Signal-to-Noise Ratio (PSNR) on the chest CBCT dataset.