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X-ray phase-contrast imaging has become indispensable for visualizing sampleswith low absorption contrast. In this regard, speckle-based techniques haveshown significant advantages in spatial resolution, phase sensitivity, andimplementation flexibility compared with traditional methods. However, theircomputational cost has hindered their wider adoption. By exploiting the powerof deep learning, we developed a new speckle-based phase-contrast imagingneural network (SPINNet) that boosts the phase retrieval speed by at least twoorders of magnitude compared to existing methods. To achieve this performance,we combined SPINNet with a novel coded-mask-based technique, an enhancedversion of the speckle-based method. Using this scheme, we demonstrate asimultaneous reconstruction of absorption and phase images on the order of 100ms, where a traditional correlation-based analysis would take several minuteseven with a cluster. In addition to significant improvement in speed, ourexperimental results show that the imaging resolution and phase retrievalquality of SPINNet outperform existing single-shot speckle-based methods.Furthermore, we successfully demonstrate its application in 3D X-rayphase-contrast tomography. Our result shows that SPINNet could enable manyapplications requiring high-resolution and fast data acquisition andprocessing, such as in-situ and in-operando 2D and 3D phase-contrast imagingand real-time at-wavelength metrology and wavefront sensing.

Real-time X-ray Phase-contrast Imaging Using SPINNet -- A Speckle-based Phase-contrast Imaging Neural Network