New reconstruction method for few-view grating-based phase-contrast imaging via dictionary learning

Grating-based phase-contrast is a hot topic in recent years owing to its excellent imaging contrast capability on soft tissues. Although it is compatible with conventional X-ray tubes and applicable in many fields, long scanning time, and high radiation dose obstruct its wider use in clinical and medical fields, especially for computed tomography applications. In this study, we solve this challenge by reducing the projection views and compensating the loss of reconstruction quality through dual-dictionary learning algorithm. The algorithm is implemented in two steps. First, estimated high-quality absorption images are obtained from the first dual-quality dictionary learning, which uses the correspondence between high-quality images and low-quality ones reconstructed from highly under-sampled data. Then, the second absorption-phase dual-modality dictionary learning is adopted to yield both estimated phase and absorption images, resulting in complementary information for both modality images. Afterwards the absorption and phase images are gradually improved in iterative reconstructions. By using SSIM RMSE measurements and visual assessment for enlarged regions of interest, our proposed method can improve the resolution of these two modality images and recover smaller structures, as compared to conventional methods.