Prior information-based high-resolution tomography image reconstruction from a single digitally reconstructed radiograph

Abstract Tomography images are essential for clinical diagnosis and trauma surgery, allowing doctors to understand the internal information of patients in more detail. Since the large amount of x-ray radiation from the continuous imaging during the process of computed tomography scanning can cause serious harm to the human body, reconstructing tomographic images from sparse views becomes a potential solution to this problem. Here we present a deep-learning framework for tomography image reconstruction, namely TIReconNet, which defines image reconstruction as a data-driven supervised learning task that allows a mapping between the 2D projection view and the 3D volume to emerge from corpus. The proposed framework consists of four parts: feature extraction module, shape mapping module, volume generation module and super resolution module. The proposed framework combines 2D and 3D operations, which can generate high-resolution tomographic images with a relatively small amount of computing resources and maintain spatial information. The proposed method is verified on chest digitally reconstructed radiographs, and the reconstructed tomography images have achieved PSNR value of 18.621 ± 1.228 dB and SSIM value of 0.872 ± 0.041 when compared against the ground truth. In conclusion, an innovative convolutional neural network architecture is proposed and validated in this study, which proves that there is the potential to generate a 3D high-resolution tomographic image from a single 2D image using deep learning. This method may actively promote the application of reconstruction technology for radiation reduction, and further exploration of intraoperative guidance in trauma and orthopedics.