学术文献库

X-ray phase-contrast imaging has become indispensable for visualizing samples with low absorption contrast. In this regard, speckle-based techniques have shown significant advantages in spatial resolution, phase sensitivity, and implementation flexibility compared with traditional methods. However, their computational cost has hindered their wider adoption. By exploiting the power of deep learning, we developed a new speckle-based phase-contrast imaging neural network (SPINNet) that boosts the phase retrieval speed by at least two orders of magnitude compared to existing methods. To achieve this performance, we combined SPINNet with a novel coded-mask-based technique, an enhanced version of the speckle-based method. Using this scheme, we demonstrate a simultaneous reconstruction of absorption and phase images on the order of 100 ms, where a traditional correlation-based analysis would take several minutes even with a cluster. In addition to significant improvement in speed, our experimental results show that the imaging resolution and phase retrieval quality of SPINNet outperform existing single-shot speckle-based methods. Furthermore, we successfully demonstrate its application in 3D X-ray phase-contrast tomography. Our result shows that SPINNet could enable many applications requiring high-resolution and fast data acquisition and processing, such as in-situ and in-operando 2D and 3D phase-contrast imaging and real-time at-wavelength metrology and wavefront sensing.