Circular LSTM for Low-Dose Sinograms Inpainting

Abstract

Computed tomography (CT) is usually accompanied by a long scanning time and substantial patient radiation exposure. Sinograms are the basis for constructing CT scans; however, continuous sinograms may highly overlap, resulting in extra radiation exposure. This paper proposes a deep learning model to inpaint a sparse-view sinogram sequence. Because a sinogram sequence around the human body is circular in nature, we propose a circular LSTM (CirLSTM) architecture that feeds position-relevant information to our model. To evaluate the performance of our proposed method, we compared the results of our inpainted sinograms with ground truth sinograms using evaluation metrics, including the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). The SSIM values for both our proposed method and the state-of-the-art method range from 0.998 to 0.999, indicating that the prediction of structures is not challenging for either method. Our proposed CirLSTM achieves PSNR values ranging from 49 to 52, outperforming all the other compared methods. These results demonstrate the feasibility of using only interleaved sinograms to construct a complete sinogram sequence and to generate high-quality CT images. Furthermore, we validated the proposed model across different body portions and CT machine models. The results show that CirLSTM outperforms all other methods in both the across-body segment validation and across-machine validation scenarios.

Keywords

• Computed tomography (CT)
• deep learning
• inpainting
• long short-term memory (LSTM)
• medical imaging
• sinogram