Restoration of Full Data from Sparse Data in Low-Dose Chest Digital Tomosynthesis Using Deep Convolutional Neural Networks

Chest digital tomosynthesis (CDT) provides more limited image information required for diagnosis when compared to computed tomography. Moreover, the radiation dose received by patients is higher in CDT than in chest radiography. Thus, CDT has not been actively used in clinical practice. To increase the usefulness of CDT, the radiation dose should reduce to the level used in chest radiography. Given the trade-off between image quality and radiation dose in medical imaging, a strategy to generating high-quality images from limited data is need. We investigated a novel approach for acquiring low-dose CDT images based on learning-based algorithms, such as deep convolutional neural networks. We used both simulation and experimental imaging data and focused on restoring reconstructed images from sparse to full sampling data. We developed a deep learning model based on end-to-end image translation using U-net. We used 11 and 81 CDT reconstructed input and output images, respectively, to develop the model. To measure the radiation dose of the proposed method, we investigated effective doses using Monte Carlo simulations. The proposed deep learning model effectively restored images with degraded quality due to lack of sampling data. Quantitative evaluation using structure similarity index measure (SSIM) confirmed that SSIM was increased by approximately 20% when using the proposed method. The effective dose required when using sparse sampling data was approximately 0.11 mSv, similar to that used in chest radiography (0.1 mSv) based on a report by the Radiation Society of North America. We investigated a new approach for reconstructing tomosynthesis images using sparse projection data. The model-based iterative reconstruction method has previously been used for conventional sparse sampling reconstruction. However, model-based computing requires high computational power, which limits fast three-dimensional image reconstruction and thus clinical applicability. We expect that the proposed learning-based reconstruction strategy will generate images with excellent quality quickly and thus have the potential for clinical use.